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Relevant bibliographies by topics / 3D point clouds

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Academic literature on the topic '3D point clouds'

Author: Grafiati

Published: 4 June 2021

Last updated: 7 July 2024

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Journal articles on the topic "3D point clouds"

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Roopa B S, Pramod Kumar S, Prema K N, and Smitha S M. "Review on 3D Point Cloud." Global Journal of Engineering and Technology Advances 16, no. 3 (September 30, 2023): 219–23. http://dx.doi.org/10.30574/gjeta.2023.16.3.0192.

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A collection of multidimensional points is represented by a data structure called a point cloud, which is frequently used to describe 3-D data. A point cloud is, technically speaking, a database of points in a three-dimensional coordinate system. However, from the viewpoint of a typical workflow, the only thing that matters is that a point cloud is an extremely accurate digital record of an item or region. It is saved as a very large number of points that cover a sensed object's surfaces. 3D point clouds have drawn more and more attention as a novel way to depict objects in recent years. In this paper, a brief introduction to point clouds is given. The study provides a discussion of point clouds, point cloud data collecting, processing, and applications.

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Liu, Ruyu, Zhiyong Zhang, Liting Dai, Guodao Zhang, and Bo Sun. "MFTR-Net: A Multi-Level Features Network with Targeted Regularization for Large-Scale Point Cloud Classification." Sensors 23, no. 8 (April 10, 2023): 3869. http://dx.doi.org/10.3390/s23083869.

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There are some irregular and disordered noise points in large-scale point clouds, and the accuracy of existing large-scale point cloud classification methods still needs further improvement. This paper proposes a network named MFTR-Net, which considers the local point cloud’s eigenvalue calculation. The eigenvalues of 3D point cloud data and the 2D eigenvalues of projected point clouds on different planes are calculated to express the local feature relationship between adjacent point clouds. A regular point cloud feature image is constructed and inputs into the designed convolutional neural network. The network adds TargetDrop to be more robust. The experimental result shows that our methods can learn more high-dimensional feature information, further improving point cloud classification, and our approach can achieve 98.0% accuracy with the Oakland 3D dataset.

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Giang, Truong Thi Huong, and Young-Jae Ryoo. "Pruning Points Detection of Sweet Pepper Plants Using 3D Point Clouds and Semantic Segmentation Neural Network." Sensors 23, no. 8 (April 17, 2023): 4040. http://dx.doi.org/10.3390/s23084040.

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Automation in agriculture can save labor and raise productivity. Our research aims to have robots prune sweet pepper plants automatically in smart farms. In previous research, we studied detecting plant parts by a semantic segmentation neural network. Additionally, in this research, we detect the pruning points of leaves in 3D space by using 3D point clouds. Robot arms can move to these positions and cut the leaves. We proposed a method to create 3D point clouds of sweet peppers by applying semantic segmentation neural networks, the ICP algorithm, and ORB-SLAM3, a visual SLAM application with a LiDAR camera. This 3D point cloud consists of plant parts that have been recognized by the neural network. We also present a method to detect the leaf pruning points in 2D images and 3D space by using 3D point clouds. Furthermore, the PCL library was used to visualize the 3D point clouds and the pruning points. Many experiments are conducted to show the method’s stability and correctness.

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Rai, A., N. Srivastava, K. Khoshelham, and K. Jain. "SEMANTIC ENRICHMENT OF 3D POINT CLOUDS USING 2D IMAGE SEGMENTATION." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVIII-1/W2-2023 (December 14, 2023): 1659–66. http://dx.doi.org/10.5194/isprs-archives-xlviii-1-w2-2023-1659-2023.

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Abstract. 3D point cloud segmentation is computationally intensive due to the lack of inherent structural information and the unstructured nature of the point cloud data, which hinders the identification and connection of neighboring points. Understanding the structure of the point cloud data plays a crucial role in obtaining a meaningful and accurate representation of the underlying 3D environment. In this paper, we propose an algorithm that builds on existing state-of-the-art techniques of 2D image segmentation and point cloud registration to enrich point clouds with semantic information. DeepLab2 with ResNet50 as backbone architecture trained on the COCO dataset is used for indoor scene semantic segmentation into several classes like wall, floor, ceiling, doors, and windows. Semantic information from 2D images is propagated along with other input data, i.e., RGB images, depth images, and sensor information to generate 3D point clouds with semantic information. Iterative Closest Point (ICP) algorithm is used for the pair-wise registration of consecutive point clouds and finally, optimization is applied using the pose graph optimization on the whole set of point clouds to generate the combined point cloud of the whole scene. 3D point cloud of the whole scene contains pseudo-color information which denotes the semantic class to which each point belongs. The proposed methodology use an off-the-shelf 2D semantic segmentation deep learning model to semantically segment 3D point clouds collected using handheld mobile LiDAR sensor. We demonstrate a comparison of the accuracy achieved compared to a manually segmented point cloud on an in-house dataset as well as a 2D3DS benchmark dataset.

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Han, Ming, Jianjun Sha, Yanheng Wang, and Xiangwei Wang. "PBFormer: Point and Bi-Spatiotemporal Transformer for Pointwise Change Detection of 3D Urban Point Clouds." Remote Sensing 15, no. 9 (April 27, 2023): 2314. http://dx.doi.org/10.3390/rs15092314.

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Change detection (CD) is a technique widely used in remote sensing for identifying the differences between data acquired at different times. Most existing 3D CD approaches voxelize point clouds into 3D grids, project them into 2D images, or rasterize them into digital surface models due to the irregular format of point clouds and the variety of changes in three-dimensional (3D) objects. However, the details of the geometric structure and spatiotemporal sequence information may not be fully utilized. In this article, we propose PBFormer, a transformer network with Siamese architecture, for directly inferring pointwise changes in bi-temporal 3D point clouds. First, we extract point sequences from irregular 3D point clouds using the k-nearest neighbor method. Second, we uniquely use a point transformer network as an encoder to extract point feature information from bitemporal 3D point clouds. Then, we design a module for fusing the spatiotemporal features of bi-temporal point clouds to effectively detect change features. Finally, multilayer perceptrons are used to obtain the CD results. Extensive experiments conducted on the Urb3DCD benchmark show that PBFormer outperforms other excellent approaches for 3D point cloud CD tasks.

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Bello, Saifullahi Aminu, Shangshu Yu, Cheng Wang, Jibril Muhmmad Adam, and Jonathan Li. "Review: Deep Learning on 3D Point Clouds." Remote Sensing 12, no. 11 (May 28, 2020): 1729. http://dx.doi.org/10.3390/rs12111729.

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A point cloud is a set of points defined in a 3D metric space. Point clouds have become one of the most significant data formats for 3D representation and are gaining increased popularity as a result of the increased availability of acquisition devices, as well as seeing increased application in areas such as robotics, autonomous driving, and augmented and virtual reality. Deep learning is now the most powerful tool for data processing in computer vision and is becoming the most preferred technique for tasks such as classification, segmentation, and detection. While deep learning techniques are mainly applied to data with a structured grid, the point cloud, on the other hand, is unstructured. The unstructuredness of point clouds makes the use of deep learning for its direct processing very challenging. This paper contains a review of the recent state-of-the-art deep learning techniques, mainly focusing on raw point cloud data. The initial work on deep learning directly with raw point cloud data did not model local regions; therefore, subsequent approaches model local regions through sampling and grouping. More recently, several approaches have been proposed that not only model the local regions but also explore the correlation between points in the local regions. From the survey, we conclude that approaches that model local regions and take into account the correlation between points in the local regions perform better. Contrary to existing reviews, this paper provides a general structure for learning with raw point clouds, and various methods were compared based on the general structure. This work also introduces the popular 3D point cloud benchmark datasets and discusses the application of deep learning in popular 3D vision tasks, including classification, segmentation, and detection.

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Mwangangi, K. K., P. O. Mc’Okeyo, S. J. Oude Elberink, and F. Nex. "EXPLORING THE POTENTIALS OF UAV PHOTOGRAMMETRIC POINT CLOUDS IN FAÇADE DETECTION AND 3D RECONSTRUCTION OF BUILDINGS." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B2-2022 (May 30, 2022): 433–40. http://dx.doi.org/10.5194/isprs-archives-xliii-b2-2022-433-2022.

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Abstract. The use of Airborne Laser Scanner (ALS) point clouds has dominated 3D buildings reconstruction research, thus giving photogrammetric point clouds less attention. Point cloud density, occlusion and vegetation cover are some of the concerns that promote the necessity to understand and question the completeness and correctness of UAV photogrammetric point clouds for 3D buildings reconstruction. This research explores the potentials of modelling 3D buildings from nadir and oblique UAV image data vis a vis airborne laser data. Optimal parameter settings for dense matching and reconstruction are analysed for both UAV image-based and lidar point clouds. This research employs an automatic data driven model approach to 3D building reconstruction. A proper segmentation into planar roof faces is crucial, followed by façade detection to capture the real extent of the buildings’ roof overhang. An analysis of the quality of point density and point noise, in relation to setting parameter indicates that with a minimum of 50 points/m2, most of the planar surfaces are reconstructed comfortably. But for smaller features than dormers on the roof, a denser point cloud than 80 points/m2 is needed. 3D buildings from UAVs point cloud can be improved by enhancing roof boundary by use of edge information from images. It can also be improved by merging the imagery building outlines, point clouds roof boundary and the walls outline to extract the real extent of the building.

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Li, Weite, Kyoko Hasegawa, Liang Li, Akihiro Tsukamoto, and Satoshi Tanaka. "Deep Learning-Based Point Upsampling for Edge Enhancement of 3D-Scanned Data and Its Application to Transparent Visualization." Remote Sensing 13, no. 13 (June 28, 2021): 2526. http://dx.doi.org/10.3390/rs13132526.

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Large-scale 3D-scanned point clouds enable the accurate and easy recording of complex 3D objects in the real world. The acquired point clouds often describe both the surficial and internal 3D structure of the scanned objects. The recently proposed edge-highlighted transparent visualization method is effective for recognizing the whole 3D structure of such point clouds. This visualization utilizes the degree of opacity for highlighting edges of the 3D-scanned objects, and it realizes clear transparent viewing of the entire 3D structures. However, for 3D-scanned point clouds, the quality of any edge-highlighting visualization depends on the distribution of the extracted edge points. Insufficient density, sparseness, or partial defects in the edge points can lead to unclear edge visualization. Therefore, in this paper, we propose a deep learning-based upsampling method focusing on the edge regions of 3D-scanned point clouds to generate more edge points during the 3D-edge upsampling task. The proposed upsampling network dramatically improves the point-distributional density, uniformity, and connectivity in the edge regions. The results on synthetic and scanned edge data show that our method can improve the percentage of edge points more than 15% compared to the existing point cloud upsampling network. Our upsampling network works well for both sharp and soft edges. A combined use with a noise-eliminating filter also works well. We demonstrate the effectiveness of our upsampling network by applying it to various real 3D-scanned point clouds. We also prove that the improved edge point distribution can improve the visibility of the edge-highlighted transparent visualization of complex 3D-scanned objects.

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Takahashi, G., and H. Masuda. "TRAJECTORY-BASED VISUALIZATION OF MMS POINT CLOUDS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W13 (June 5, 2019): 1127–33. http://dx.doi.org/10.5194/isprs-archives-xlii-2-w13-1127-2019.

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<p><strong>Abstract.</strong> MMSs allow us to obtain detailed 3D information around roads. Especially, LiDAR point clouds can be used for map generation and infrastructure management. For practical uses, however, it is necessary to add labels to a part of the points since various objects can be included in the point clouds. Existing automatic classification methods are not completely error-free, and may incorrectly classify objects. Therefore, even though automatic methods are applied to the point clouds, operators have to verify the labels. While operators classify the point clouds manually, selecting 3D points tasks in 3D views are difficult. In this paper, we propose a new point-cloud image based on the trajectories of MMSs. We call our point-cloud image <i>trajectory-based point-cloud image</i>. Although the image is distorted because it is generated based on rotation angles of laser scanners, we confirmed that most objects can be recognized from point-cloud images by checking main road facilities. We evaluated how efficient the annotation can be done using our method, and the results show that operators could add annotations to point-cloud images more efficiently.</p>

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Barnefske, Eike, and Harald Sternberg. "Evaluating the Quality of Semantic Segmented 3D Point Clouds." Remote Sensing 14, no. 3 (January 18, 2022): 446. http://dx.doi.org/10.3390/rs14030446.

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Recently, 3D point clouds have become a quasi-standard for digitization. Point cloud processing remains a challenge due to the complex and unstructured nature of point clouds. Currently, most automatic point cloud segmentation methods are data-based and gain knowledge from manually segmented ground truth (GT) point clouds. The creation of GT point clouds by capturing data with an optical sensor and then performing a manual or semi-automatic segmentation is a less studied research field. Usually, GT point clouds are semantically segmented only once and considered to be free of semantic errors. In this work, it is shown that this assumption has no overall validity if the reality is to be represented by a semantic point cloud. Our quality model has been developed to describe and evaluate semantic GT point clouds and their manual creation processes. It is applied on our dataset and publicly available point cloud datasets. Furthermore, we believe that this quality model contributes to the objective evaluation and comparability of data-based segmentation algorithms.

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Dissertations / Theses on the topic "3D point clouds"

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Srivastava, Siddharth. "Features for 3D point clouds." Thesis, IIT Delhi, 2019. http://eprint.iitd.ac.in:80//handle/2074/8061.

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Filho, Carlos André Braile Przewodowski. "Feature extraction from 3D point clouds." Universidade de São Paulo, 2018. http://www.teses.usp.br/teses/disponiveis/55/55134/tde-30072018-111718/.

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Computer vision is a research field in which images are the main object of study. One of its category of problems is shape description. Object classification is one important example of applications using shape descriptors. Usually, these processes were performed on 2D images. With the large-scale development of new technologies and the affordable price of equipment that generates 3D images, computer vision has adapted to this new scenario, expanding the classic 2D methods to 3D. However, it is important to highlight that 2D methods are mostly dependent on the variation of illumination and color, while 3D sensors provide depth, structure/3D shape and topological information beyond color. Thus, different methods of shape descriptors and robust attributes extraction were studied, from which new attribute extraction methods have been proposed and described based on 3D data. The results obtained from well known public datasets have demonstrated their efficiency and that they compete with other state-of-the-art methods in this area: the RPHSD (a method proposed in this dissertation), achieved 85:4% of accuracy on the University of Washington RGB-D dataset, being the second best accuracy on this dataset; the COMSD (another proposed method) has achieved 82:3% of accuracy, standing at the seventh position in the rank; and the CNSD (another proposed method) at the ninth position. Also, the RPHSD and COMSD methods have relatively small processing complexity, so they achieve high accuracy with low computing time.
Visão computacional é uma área de pesquisa em que as imagens são o principal objeto de estudo. Um dos problemas abordados é o da descrição de formatos (em inglês, shapes). Classificação de objetos é um importante exemplo de aplicação que usa descritores de shapes. Classicamente, esses processos eram realizados em imagens 2D. Com o desenvolvimento em larga escala de novas tecnologias e o barateamento dos equipamentos que geram imagens 3D, a visão computacional se adaptou para este novo cenário, expandindo os métodos 2D clássicos para 3D. Entretanto, estes métodos são, majoritariamente, dependentes da variação de iluminação e de cor, enquanto os sensores 3D fornecem informações de profundidade, shape 3D e topologia, além da cor. Assim, foram estudados diferentes métodos de classificação de objetos e extração de atributos robustos, onde a partir destes são propostos e descritos novos métodos de extração de atributos a partir de dados 3D. Os resultados obtidos utilizando bases de dados 3D públicas conhecidas demonstraram a eficiência dos métodos propóstos e que os mesmos competem com outros métodos no estado-da-arte: o RPHSD (um dos métodos propostos) atingiu 85:4% de acurácia, sendo a segunda maior acurácia neste banco de dados; o COMSD (outro método proposto) atingiu 82:3% de acurácia, se posicionando na sétima posição do ranking; e o CNSD (outro método proposto) em nono lugar. Além disso, os métodos RPHSD têm uma complexidade de processamento relativamente baixa. Assim, eles atingem uma alta acurácia com um pequeno tempo de processamento.

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Truong, Quoc Hung. "Knowledge-based 3D point clouds processing." Phd thesis, Université de Bourgogne, 2013. http://tel.archives-ouvertes.fr/tel-00977434.

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The modeling of real-world scenes through capturing 3D digital data has proven to be both useful andapplicable in a variety of industrial and surveying applications. Entire scenes are generally capturedby laser scanners and represented by large unorganized point clouds possibly along with additionalphotogrammetric data. A typical challenge in processing such point clouds and data lies in detectingand classifying objects that are present in the scene. In addition to the presence of noise, occlusionsand missing data, such tasks are often hindered by the irregularity of the capturing conditions bothwithin the same dataset and from one data set to another. Given the complexity of the underlyingproblems, recent processing approaches attempt to exploit semantic knowledge for identifying andclassifying objects. In the present thesis, we propose a novel approach that makes use of intelligentknowledge management strategies for processing of 3D point clouds as well as identifying andclassifying objects in digitized scenes. Our approach extends the use of semantic knowledge to allstages of the processing, including the guidance of the individual data-driven processing algorithms.The complete solution consists in a multi-stage iterative concept based on three factors: the modeledknowledge, the package of algorithms, and a classification engine. The goal of the present work isto select and guide algorithms following an adaptive and intelligent strategy for detecting objects inpoint clouds. Experiments with two case studies demonstrate the applicability of our approach. Thestudies were carried out on scans of the waiting area of an airport and along the tracks of a railway.In both cases the goal was to detect and identify objects within a defined area. Results show that ourapproach succeeded in identifying the objects of interest while using various data types

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Stålberg, Martin. "Reconstruction of trees from 3D point clouds." Thesis, Uppsala universitet, Avdelningen för systemteknik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-316833.

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The geometrical structure of a tree can consist of thousands, even millions, of branches, twigs and leaves in complex arrangements. The structure contains a lot of useful information and can be used for example to assess a tree's health or calculate parameters such as total wood volume or branch size distribution. Because of the complexity, capturing the structure of an entire tree used to be nearly impossible, but the increased availability and quality of particularly digital cameras and Light Detection and Ranging (LIDAR) instruments is making it increasingly possible. A set of digital images of a tree, or a point cloud of a tree from a LIDAR scan, contains a lot of data, but the information about the tree structure has to be extracted from this data through analysis. This work presents a method of reconstructing 3D models of trees from point clouds. The model is constructed from cylindrical segments which are added one by one. Bayesian inference is used to determine how to optimize the parameters of model segment candidates and whether or not to accept them as part of the model. A Hough transform for finding cylinders in point clouds is presented, and used as a heuristic to guide the proposals of model segment candidates. Previous related works have mainly focused on high density point clouds of sparse trees, whereas the objective of this work was to analyze low resolution point clouds of dense almond trees. The method is evaluated on artificial and real datasets and works rather well on high quality data, but performs poorly on low resolution data with gaps and occlusions.

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Salman, Nader. "From 3D point clouds to feature preserving meshes." Nice, 2010. http://www.theses.fr/2010NICE4086.

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La majorité des algorithmes de reconstruction de surface sont optimisés pour s’appliquer à des données de haute qualité. Les résultats obtenus peuvent alors être utilisables si les données proviennent de solutions d’acquisition bon marché. Notre première contribution est un algorithme de reconstruction de surfaces à partir de données de stéréo vision. Il combine les informations liées aux points 3D avec les images calibrées afin de combler l’imprécision des données. L’algorithme construit une soupe de triangles 3D à l’aide des images calibrées et à l’issue d’une phase de prétraitement du nuage de points. Pour épouser au mieux la surface de la scène, on contraint cette soupe de triangle 3D à respecter des critères de visibilité et de photo-consistance. On calcule ensuite un maillage à partir de la soupe de triangles à l’aide d’une technique de reconstruction qui combine les triangulations de Delaunay contraintes et le raffinement de Delaunay. Notre seconde contribution est un algorithme qui construit, à partir d’un nuage de points 3D échantillonnés sur une surface, un maillage de surface qui représente fidèlement les arrêtes vives. Cet algorithme génère un bon compromis entre précision et complexité du maillage. Dans un premier temps, on extrait une approximation des arrêtes vives de la surface sous-jacente à partir du nuage de points. Dans un deuxième temps, on utilise une variante du raffinement de Delaunay pour générer un maillage qui combine les arrêtes vives extraites avec une surface implicite obtenue à partir du nuage de points. Notre méthode se révèle flexible, robuste au bruit ; cette méthode peut prendre en compte la résolution du maillage ciblé et un champ de taille défini par l’utilisateur. Nos deux contributions génèrent des résultats efficaces sur une variété de scènes et de modèles. Notre méthode améliore l’état de l’art en termes de précision
Most of the current surface reconstruction algorithms target high quality data and can produce some intractable results when used with point clouds acquired through profitable 3D acquisitions methods. Our first contribution is a surface reconstruction, algorithm from stereo vision data copes with the data’s fuzziness using information from both the acquired D point cloud and the calibrated images. After pre-processing the point cloud, the algorithm builds, using the calibrated images, 3D triangular soup consistent with the surface of the scene through a combination of visibility and photo-consistency constraints. A mesh is then computed from the triangle soup using a combination of restricted Delaunay triangulation and Delaunay refinement methods. Our second contribution is an algorithm that builds, given a 3D point cloud sampled on a surface, an approximating surface mesh with an accurate representation of surface sharp edges, providing an enhanced trade-off between accuracy and mesh complexity. We first extract from the point cloud an approximation of the sharp edges of the underlying surface. Then a feature preserving variant of a Delaunay refinement process generates a mesh combining a faithful representation of the extracted sharp edges with an implicit surface obtained from the point cloud. The method is shown to be flexible, robust to noise and tuneable to adapt to the scale of the targeted mesh and to a user defined sizing field. We demonstrate the effectiveness of both contributions on a variety of scenes and models acquired with different hardware and show results that compare favourably, in terms of accuracy, with the current state of the art

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Robert, Damien. "Efficient learning on large-scale 3D point clouds." Electronic Thesis or Diss., Université Gustave Eiffel, 2024. http://www.theses.fr/2024UEFL2003.

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Au cours de la dernière décennie, l'apprentissage profond a été le moteur des progrès dans l'analyse automatisée de structures de données complexes aussi diverses que le texte, l'image, l'audio et la vidéo. En particulier, les modèles de type transformer et l'apprentissage auto-supervisé ont récemment déclenché une course généralisée visant à apprendre des représentations textuelles et visuelles expressives en entrainant le modèle au plus grand nombre de paramètres, sur le plus gros jeu de données possible, à l'aide des plus grandes ressources de calcul. Cette thèse emprunte un chemin différent en proposant des méthodes d'apprentissage profond économes en ressources, pour l'analyse de nuages de points 3D à grande échelle. L'efficacité des approches présentées se décline sous différentes formes : entrainement rapide, peu de paramètres, faible coût de calcul, économe en mémoire et exploitation de données disponibles de manière réaliste. Ce faisant, nous nous efforçons de concevoir des solutions pouvant être utilisées par les chercheurs et les praticiens avec des exigences matérielles minimales.Nous introduisons d'abord un modèle de segmentation sémantique 3D qui combine l'efficacité des méthodes basées superpoints avec l'expressivité des transformers. Nous construisons une représentation hiérarchique des données qui réduit considérablement la taille du problème d'analyse de nuage de points 3D, facilitant le traitement de scènes de grande échelle.Notre réseau se révèle égaler, voire surpasser, les approches de pointe sur une gamme de capteurs et d'environnements d'acquisition, tout en réduisant le nombre de paramètres et le temps d'entrainement de un à deux ordres de grandeur. Nous étendons ensuite ce cadre à la segmentation panoptique de nuages de points à grande échelle.Les méthodes existantes de segmentation d'instance et panoptique doivent résoudre un problème de correspondance complexe entre les instances prédites et réelles pour calculer leur fonction de coût. Au lieu de cela, nous formulons cette tâche comme un problème de clustering de graphe, qu'un petit réseau est entrainé pour résoudre à partir d'objectifs locaux uniquement, sans nécessiter le calcul d'instances durant l'entraînement. Notre modèle peut traiter des scènes de dix millions de points à la fois sur un seul GPU en quelques secondes, ouvrant la voie à la segmentation panoptique 3D à des échelles sans précédent. Enfin, nous proposons d'exploiter la complémentarité des modalités image et nuage de points pour améliorer l'analyse de scènes 3D. Nous nous plaçons dans un cadre d'acquisition réaliste, où plusieurs images arbitrairement positionnées observent la même scène, avec de potentielles occultations. Contrairement aux approches existantes de fusion 2D-3D, nous apprenons à sélectionner des informations à partir de différentes vues du même objet en fonction de leurs conditions d'observation respectives : distance caméra-objet, taux d'occultation, distorsion optique, etc. Notre implémentation efficace atteint l'état de l'art tant pour des scènes d'intérieur que d'extérieur, avec des exigences minimales : nuages de points bruts, images positionnées de manière arbitraire et les poses de leurs caméras. Dans l'ensemble, cette thèse soutient le principe que, dans des régimes où les données sont rares, exploiter la structure du problème permet de développer des architectures à la fois efficaces et performantes
For the past decade, deep learning has been driving progress in the automated understanding of complex data structures as diverse as text, image, audio, and video. In particular, transformer-based models and self-supervised learning have recently ignited a global competition to learn expressive textual and visual representations by training the largest possible model on Internet-scale datasets, with the help of massive computational resources. This thesis takes a different path, by proposing resource-efficient deep learning methods for the analysis of large-scale 3D point clouds.The efficiency of the introduced approaches comes in various flavors: fast training, few parameters, small compute or memory footprint, and leveraging realistically-available data.In doing so, we strive to devise solutions that can be used by researchers and practitioners with minimal hardware requirements.We first introduce a 3D semantic segmentation model which combines the efficiency of superpoint-based methods with the expressivity of transformers. We build a hierarchical data representation which drastically reduces the size of the 3D point cloud parsing problem, facilitating the processing of large point clouds en masse. Our self-attentive network proves to match or even surpass state-of-the-art approaches on a range of sensors and acquisition environments, while boasting orders of magnitude fewer parameters, faster training, and swift inference.We then build upon this framework to tackle panoptic segmentation of large-scale point clouds. Existing instance and panoptic segmentation methods need to solve a complex matching problem between predicted and ground truth instances for computing their supervision loss.Instead, we frame this task as a scalable graph clustering problem, which a small network is trained to address from local objectives only, without computing the actual object instances at train time. Our lightweight model can process ten-million-point scenes at once on a single GPU in a few seconds, opening the door to 3D panoptic segmentation at unprecedented scales. Finally, we propose to exploit the complementarity of image and point cloud modalities to enhance 3D scene understanding.We place ourselves in a realistic acquisition setting where multiple arbitrarily-located images observe the same scene, with potential occlusions.Unlike previous 2D-3D fusion approaches, we learn to select information from various views of the same object based on their respective observation conditions: camera-to-object distance, occlusion rate, optical distortion, etc. Our efficient implementation achieves state-of-the-art results both in indoor and outdoor settings, with minimal requirements: raw point clouds, arbitrarily-positioned images, and their cameras poses. Overall, this thesis upholds the principle that in data-scarce regimes,exploiting the structure of the problem unlocks both efficient and performant architectures

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Al, Hakim Ezeddin. "3D YOLO: End-to-End 3D Object Detection Using Point Clouds." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-234242.

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For safe and reliable driving, it is essential that an autonomous vehicle can accurately perceive the surrounding environment. Modern sensor technologies used for perception, such as LiDAR and RADAR, deliver a large set of 3D measurement points known as a point cloud. There is a huge need to interpret the point cloud data to detect other road users, such as vehicles and pedestrians. Many research studies have proposed image-based models for 2D object detection. This thesis takes it a step further and aims to develop a LiDAR-based 3D object detection model that operates in real-time, with emphasis on autonomous driving scenarios. We propose 3D YOLO, an extension of YOLO (You Only Look Once), which is one of the fastest state-of-the-art 2D object detectors for images. The proposed model takes point cloud data as input and outputs 3D bounding boxes with class scores in real-time. Most of the existing 3D object detectors use hand-crafted features, while our model follows the end-to-end learning fashion, which removes manual feature engineering. 3D YOLO pipeline consists of two networks: (a) Feature Learning Network, an artificial neural network that transforms the input point cloud to a new feature space; (b) 3DNet, a novel convolutional neural network architecture based on YOLO that learns the shape description of the objects. Our experiments on the KITTI dataset shows that the 3D YOLO has high accuracy and outperforms the state-of-the-art LiDAR-based models in efficiency. This makes it a suitable candidate for deployment in autonomous vehicles.
För att autonoma fordon ska ha en god uppfattning av sin omgivning används moderna sensorer som LiDAR och RADAR. Dessa genererar en stor mängd 3-dimensionella datapunkter som kallas point clouds. Inom utvecklingen av autonoma fordon finns det ett stort behov av att tolka LiDAR-data samt klassificera medtrafikanter. Ett stort antal studier har gjorts om 2D-objektdetektering som analyserar bilder för att upptäcka fordon, men vi är intresserade av 3D-objektdetektering med hjälp av endast LiDAR data. Därför introducerar vi modellen 3D YOLO, som bygger på YOLO (You Only Look Once), som är en av de snabbaste state-of-the-art modellerna inom 2D-objektdetektering för bilder. 3D YOLO tar in ett point cloud och producerar 3D lådor som markerar de olika objekten samt anger objektets kategori. Vi har tränat och evaluerat modellen med den publika träningsdatan KITTI. Våra resultat visar att 3D YOLO är snabbare än dagens state-of-the-art LiDAR-baserade modeller med en hög träffsäkerhet. Detta gör den till en god kandidat för kunna användas av autonoma fordon.

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Biasutti, Pierre. "2D Image Processing Applied to 3D LiDAR Point Clouds." Thesis, Bordeaux, 2019. http://www.theses.fr/2019BORD0161/document.

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L'intérêt toujours grandissant pour les données cartographiques fiables, notamment en milieu urbain, a motivé le développement de systèmes de cartographie mobiles terrestres. Ces systèmes sont conçus pour l'acquisition de données de très haute précision, telles que des nuages de points LiDAR 3D et des images optiques. La multitude de données, ainsi que leur diversité, rendent complexe le traitement des données issues de ce type de systèmes. Cette thèse se place dans le contexte du traitement de l'image appliqué au nuages de points LiDAR 3D issus de ce type de système.Premièrement, nous nous intéressons à des images issues de la projection de nuages de points LiDAR dans des grilles de pixels 2D régulières. Ces projections créent généralement des images éparses, dans lesquelles l'information de certains pixels n'est pas connue. Nous proposons alors différentes méthodes pour des applications telles que la génération d'orthoimages haute résolution, l'imagerie RGB-D et l'estimation de la visibilité des points d'un nuage.De plus, nous proposons d'exploiter la topologie d'acquisition des capteurs LiDAR pour produire des images de faible résolution: les range-images. Ces images offrent une représentation efficace et canonique du nuage de points, tout en étant directement accessibles à partir du nuage de points. Nous montrons comment ces images peuvent être utilisées pour simplifier, voire améliorer, des méthodes pour le recalage multi-modal, la segmentation, la désoccultation et la détection 3D
The ever growing demand for reliable mapping data, especially in urban environments, has motivated the development of "close-range" Mobile Mapping Systems (MMS). These systems acquire high precision data, and in particular 3D LiDAR point clouds and optical images. The large amount of data, along with their diversity, make MMS data processing a very complex task. This thesis lies in the context of 2D image processing applied to 3D LiDAR point clouds acquired with MMS.First, we focus on the projection of the LiDAR point clouds onto 2D pixel grids to create images. Such projections are often sparse because some pixels do not carry any information. We use these projections for different applications such as high resolution orthoimage generation, RGB-D imaging and visibility estimation in point clouds.Moreover, we exploit the topology of LiDAR sensors in order to create low resolution images, named range-images. These images offer an efficient and canonical representation of the point cloud, while being directly accessible from the point cloud. We show how range-images can be used to simplify, and sometimes outperform, methods for multi-modal registration, segmentation, desocclusion and 3D detection

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IRFAN, MUHAMMAD ABEER. "Joint geometry and color denoising for 3D point clouds." Doctoral thesis, Politecnico di Torino, 2021. http://hdl.handle.net/11583/2912976.

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Fucili, Mattia. "3D object detection from point clouds with dense pose voters." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2019. http://amslaurea.unibo.it/17616/.

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Il riconoscimento di oggetti è sempre stato un compito sﬁdante per la Computer Vision. Trova applicazione in molti campi, principalmente nell’industria, come ad esempio per permettere ad un robot di trovare gli oggetti da afferrare. Negli ultimi decenni tali compiti hanno trovato nuovi modi di essere raggiunti grazie alla riscoperta delle Reti Neurali, in particolare le Reti Neurali Convoluzionali. Questo tipo di reti ha raggiunto ottimi risultati in molte applicazioni per il riconoscimento e la classiﬁcazione degli oggetti. La tendenza, ora, `e quella di utilizzare tali reti anche nell’industria automobilistica per cercare di rendere reale il sogno delle automobili che guidano da sole. Ci sono molti lavori importanti sul riconoscimento delle auto dalle immagini. In questa tesi presentiamo la nostra architettura di Rete Neurale Convoluzionale per il riconoscimento di automobili e la loro posizione nello spazio, utilizzando solo input lidar. Salvando le informazioni riguardanti le bounding box attorno all’auto a livello del punto ci assicura una buona previsione anche in situazioni in cui le automobili sono occluse. I test vengono eseguiti sul dataset più utilizzato per il riconoscimento di automobili e pedoni nelle applicazioni di guida autonoma.

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Books on the topic "3D point clouds"

1

S, Cheok Geraldine, and National Institute of Standards and Technology (U.S.), eds. Registering 3D point clouds: An experimental evaluation. Gaithersburg, MD: U.S. Dept. of Commerce, Technology Administration, National Institute of Standards and Technology, 2001.

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S, Cheok Geraldine, and National Institute of Standards and Technology (U.S.), eds. Registering 3D point clouds: An experimental evaluation. Gaithersburg, MD: U.S. Dept. of Commerce, Technology Administration, National Institute of Standards and Technology, 2001.

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Uray, Peter. From 3D point clouds to surfaces and volumes: Dissertation. Wien: Oldenbourg, 1997.

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National Institute of Standards and Technology (U.S.), ed. REGISTERING 3D POINT CLOUDS: AN EXPERIMENTAL EVALUATION... NISTIR 6743... U.S. DEPARTMENT OF COMMERCE. [S.l: s.n., 2001.

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Golyanik, Vladislav. Robust Methods for Dense Monocular Non-Rigid 3D Reconstruction and Alignment of Point Clouds. Wiesbaden: Springer Fachmedien Wiesbaden, 2020. http://dx.doi.org/10.1007/978-3-658-30567-3.

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Liu, Shan, Min Zhang, Pranav Kadam, and C. C. Jay Kuo. 3D Point Cloud Analysis. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-89180-0.

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Zhang, Guoxiang, and YangQuan Chen. Towards Optimal Point Cloud Processing for 3D Reconstruction. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-96110-7.

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Registering 3D point clouds: An experimental evaluation. Gaithersburg, MD: U.S. Dept. of Commerce, Technology Administration, National Institute of Standards and Technology, 2001.

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Golyanik, Vladislav. Robust Methods for Dense Monocular Non-Rigid 3D Reconstruction and Alignment of Point Clouds. Springer Vieweg, 2020.

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Chen, YangQuan, and Guoxiang Zhang. Towards Optimal Point Cloud Processing for 3D Reconstruction. Springer International Publishing AG, 2022.

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Book chapters on the topic "3D point clouds"

1

Kamberov, George, Gerda Kamberova, and Amit Jain. "3D Shape from Unorganized 3D Point Clouds." In Advances in Visual Computing, 621–29. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11595755_76.

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Su, Jingyong, and Lin-Lin Tang. "Shape Estimation from 3D Point Clouds." In Intelligent Data analysis and its Applications, Volume I, 39–46. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-07776-5_5.

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Li, Yunpeng, Noah Snavely, Daniel P. Huttenlocher, and Pascal Fua. "Worldwide Pose Estimation Using 3D Point Clouds." In Large-Scale Visual Geo-Localization, 147–63. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-25781-5_8.

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Xue, Mei, Shogo Tokai, and Hiroyuki Hase. "Point Clouds Based 3D Facial Expression Generation." In Advances in Mechanical Design, 467–84. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-6553-8_32.

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Li, Yunpeng, Noah Snavely, Dan Huttenlocher, and Pascal Fua. "Worldwide Pose Estimation Using 3D Point Clouds." In Computer Vision – ECCV 2012, 15–29. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-33718-5_2.

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Yan, Feng, Fei Wang, Yu Guo, and Peilin Jiang. "Saliency-Guided Smoothing for 3D Point Clouds." In Intelligent Computing Theories and Application, 165–74. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-63309-1_16.

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Medina, F. Patricia, and Randy Paffenroth. "Machine Learning in LiDAR 3D Point Clouds." In Association for Women in Mathematics Series, 113–33. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-79891-8_6.

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Liu, Daniel, Ronald Yu, and Hao Su. "Adversarial Shape Perturbations on 3D Point Clouds." In Computer Vision – ECCV 2020 Workshops, 88–104. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-66415-2_6.

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Hamdi, Abdullah, Sara Rojas, Ali Thabet, and Bernard Ghanem. "AdvPC: Transferable Adversarial Perturbations on 3D Point Clouds." In Computer Vision – ECCV 2020, 241–57. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-58610-2_15.

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Dyshkant, Natalia. "Comparison of Point Clouds Acquired by 3D Scanner." In Discrete Geometry for Computer Imagery, 47–58. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-37067-0_5.

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Conference papers on the topic "3D point clouds"

1

Kim, Sunghan, Mingyu Kim, Jeongtae Lee, Jinhwi Pyo, Heeyoung Heo, Dongho Yun, and Kwanghee Ko. "Registration of 3D Point Clouds for Ship Block Measurement." In SNAME 5th World Maritime Technology Conference. SNAME, 2015. http://dx.doi.org/10.5957/wmtc-2015-252.

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In this paper, a software system for registration of point clouds is developed. The system consists of two modules for registration and user interaction. The registration module contains functions for manual and automatic registration. The manual method allows a user to select feature points or planes from the point clouds manually. The selected planes or features are then processed to establish the correspondence between the point clouds, and registration is performed to obtain one large point cloud. The automatic registration uses sphere targets. Sphere targets are attached to an object of interest. A scanner measures the object as well as the targets to produce point clouds, from which the targets are extracted using shape intrinsic properties. Then correspondence between the point clouds is obtained using the targets, and the registration is performed. The user interaction module provides a GUI environment which allows a user to navigate point clouds, to compute various features, to visualize point clouds and to select/unselect points interactively and the point-processing unit containing functions for filtering, estimation of geometric features, and various data structures for managing point clouds of large size. The developed system is tested with actual measurement data of various blocks in a shipyard.

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Wells, Lee J., Mohammed S. Shafae, and Jaime A. Camelio. "Automated Part Inspection Using 3D Point Clouds." In ASME 2013 International Manufacturing Science and Engineering Conference collocated with the 41st North American Manufacturing Research Conference. American Society of Mechanical Engineers, 2013. http://dx.doi.org/10.1115/msec2013-1212.

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Ever advancing sensor and measurement technologies continually provide new opportunities for knowledge discovery and quality control (QC) strategies for complex manufacturing systems. One such state-of-the-art measurement technology currently being implemented in industry is the 3D laser scanner, which can rapidly provide millions of data points to represent an entire manufactured part’s surface. This gives 3D laser scanners a significant advantage over competing technologies that typically provide tens or hundreds of data points. Consequently, data collected from 3D laser scanners have a great potential to be used for inspecting parts for surface and feature abnormalities. The current use of 3D point clouds for part inspection falls into two main categories; 1) Extracting feature parameters, which does not complement the nature of 3D point clouds as it wastes valuable data and 2) An ad-hoc manual process where a visual representation of a point cloud (usually as deviations from nominal) is analyzed, which tends to suffer from slow, inefficient, and inconsistent inspection results. Therefore our paper proposes an approach to automate the latter approach to 3D point cloud inspection. The proposed approach uses a newly developed adaptive generalized likelihood ratio (AGLR) technique to identify the most likely size, shape, and magnitude of a potential fault within the point cloud, which transforms the ad-hoc visual inspection approach to a statistically viable automated inspection solution. In order to aid practitioners in designing and implementing an AGLR-based inspection process, our paper also reports the performance of the AGLR with respect to the probability of detecting specific size and magnitude faults in addition to the probability of a false alarms.

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Men, Hao, and Kishore Pochiraju. "Hue Assisted Registration of 3D Point Clouds." In ASME 2010 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2010. http://dx.doi.org/10.1115/detc2010-29192.

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This paper presents a variant of the Iterative Closest Point (ICP) algorithm for merging multiple color point clouds generated from a mobile 3D Light Detection and Ranging (LIDAR) System. This algorithm uses hue information generated from a camera along with the coordinates of the scan points and enables high accuracy registration of point clouds. A k-d tree based nearest neighbor search associates corresponding colored points in 4-D space between data and model point clouds. Singular Value Decomposition (SVD) method solves for the rigid rotation and translation. Experimental results illustrate that 3D color point clouds accelerate the 3D map registration if the hue data and model point clouds have sufficient hue distribution and the imaging sensor robustly captures the hue.

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4

Xiang, Chong, Charles R. Qi, and Bo Li. "Generating 3D Adversarial Point Clouds." In 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2019. http://dx.doi.org/10.1109/cvpr.2019.00935.

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5

Lihui Wang, Baozong Yuan, and Zhenjiang Miao. "3D point clouds parameterization alogrithm." In 2008 9th International Conference on Signal Processing (ICSP 2008). IEEE, 2008. http://dx.doi.org/10.1109/icosp.2008.4697396.

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Lubos, Paul, Rudiger Beimler, Markus Lammers, and Frank Steinicke. "Touching the Cloud: Bimanual annotation of immersive point clouds." In 2014 IEEE Symposium on 3D User Interfaces (3DUI). IEEE, 2014. http://dx.doi.org/10.1109/3dui.2014.6798885.

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Fu, Rao, Cheng Wen, Qian Li, Xiao Xiao, and Pierre Alliez. "BPNet: Bézier Primitive Segmentation on 3D Point Clouds." In Thirty-Second International Joint Conference on Artificial Intelligence {IJCAI-23}. California: International Joint Conferences on Artificial Intelligence Organization, 2023. http://dx.doi.org/10.24963/ijcai.2023/84.

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This paper proposes BPNet, a novel end-to-end deep learning framework to learn Bézier primitive segmentation on 3D point clouds. The existing works treat different primitive types separately, thus limiting them to finite shape categories. To address this issue, we seek a generalized primitive segmentation on point clouds. Taking inspiration from Bézier decomposition on NURBS models, we transfer it to guide point cloud segmentation casting off primitive types. A joint optimization framework is proposed to learn Bézier primitive segmentation and geometric fitting simultaneously on a cascaded architecture. Specifically, we introduce a soft voting regularizer to improve primitive segmentation and propose an auto-weight embedding module to cluster point features, making the network more robust and generic. We also introduce a reconstruction module where we successfully process multiple CAD models with different primitives simultaneously. We conducted extensive experiments on the synthetic ABC dataset and real-scan datasets to validate and compare our approach with different baseline methods. Experiments show superior performance over previous work in terms of segmentation, with a substantially faster inference speed.

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Zhang, Zihao, Lei Hu, Xiaoming Deng, and Shihong Xia. "Sequential 3D Human Pose Estimation Using Adaptive Point Cloud Sampling Strategy." In Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. California: International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/184.

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3D human pose estimation is a fundamental problem in artificial intelligence, and it has wide applications in AR/VR, HCI and robotics. However, human pose estimation from point clouds still suffers from noisy points and estimated jittery artifacts because of handcrafted-based point cloud sampling and single-frame-based estimation strategies. In this paper, we present a new perspective on the 3D human pose estimation method from point cloud sequences. To sample effective point clouds from input, we design a differentiable point cloud sampling method built on density-guided attention mechanism. To avoid the jitter caused by previous 3D human pose estimation problems, we adopt temporal information to obtain more stable results. Experiments on the ITOP dataset and the NTU-RGBD dataset demonstrate that all of our contributed components are effective, and our method can achieve state-of-the-art performance.

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Liu, Weiquan, Hanyun Guo, Weini Zhang, Yu Zang, Cheng Wang, and Jonathan Li. "TopoSeg: Topology-aware Segmentation for Point Clouds." In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. California: International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/168.

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Point cloud segmentation plays an important role in AI applications such as autonomous driving, AR, and VR. However, previous point cloud segmentation neural networks rarely pay attention to the topological correctness of the segmentation results. In this paper, focusing on the perspective of topology awareness. First, to optimize the distribution of segmented predictions from the perspective of topology, we introduce the persistent homology theory in topology into a 3D point cloud deep learning framework. Second, we propose a topology-aware 3D point cloud segmentation module, TopoSeg. Specifically, we design a topological loss function embedded in TopoSeg module, which imposes topological constraints on the segmentation of 3D point clouds. Experiments show that our proposed TopoSeg module can be easily embedded into the point cloud segmentation network and improve the segmentation performance. In addition, based on the constructed topology loss function, we propose a topology-aware point cloud edge extraction algorithm, which is demonstrated that has strong robustness.

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10

Tchapmi, Lyne, Christopher Choy, Iro Armeni, JunYoung Gwak, and Silvio Savarese. "SEGCloud: Semantic Segmentation of 3D Point Clouds." In 2017 International Conference on 3D Vision (3DV). IEEE, 2017. http://dx.doi.org/10.1109/3dv.2017.00067.

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Reports on the topic "3D point clouds"

1

Witzgall, Christoph, and Geraldine S. Cheok. Registering 3D point clouds:. Gaithersburg, MD: National Institute of Standards and Technology, 2001. http://dx.doi.org/10.6028/nist.ir.6743.

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Habib, Ayman, Darcy M. Bullock, Yi-Chun Lin, and Raja Manish. Road Ditch Line Mapping with Mobile LiDAR. Purdue University, 2021. http://dx.doi.org/10.5703/1288284317354.

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Maintenance of roadside ditches is important to avoid localized flooding and premature failure of pavements. Scheduling effective preventative maintenance requires mapping of the ditch profile to identify areas requiring excavation of long-term sediment accumulation. High-resolution, high-quality point clouds collected by mobile LiDAR mapping systems (MLMS) provide an opportunity for effective monitoring of roadside ditches and performing hydrological analyses. This study evaluated the applicability of mobile LiDAR for mapping roadside ditches for slope and drainage analyses. The performance of alternative MLMS units was performed. These MLMS included an unmanned ground vehicle, an unmanned aerial vehicle, a portable backpack system along with its vehicle-mounted version, a medium-grade wheel-based system, and a high-grade wheel-based system. Point cloud from all the MLMS units were in agreement in the vertical direction within the ±3 cm range for solid surfaces, such as paved roads, and ±7 cm range for surfaces with vegetation. The portable backpack system that could be carried by a surveyor or mounted on a vehicle and was the most flexible MLMS. The report concludes that due to flexibility and cost effectiveness of the portable backpack system, it is the preferred platform for mapping roadside ditches, followed by the medium-grade wheel-based system. Furthermore, a framework for ditch line characterization is proposed and tested using datasets acquired by the medium-grade wheel-based and vehicle-mounted portable systems over a state highway. An existing ground filtering approach is modified to handle variations in point density of mobile LiDAR data. Hydrological analyses, including flow direction and flow accumulation, are applied to extract the drainage network from the digital terrain model (DTM). Cross-sectional/longitudinal profiles of the ditch are automatically extracted from LiDAR data and visualized in 3D point clouds and 2D images. The slope derived from the LiDAR data was found to be very close to highway cross slope design standards of 2% on driving lanes, 4% on shoulders, as well as 6-by-1 slope for ditch lines. Potential flooded regions are identified by detecting areas with no LiDAR return and a recall score of 54% and 92% was achieved by the medium-grade wheel-based and vehicle-mounted portable systems, respectively. Furthermore, a framework for ditch line characterization is proposed and tested using datasets acquired by the medium-grade wheel-based and vehicle-mounted portable systems over a state highway. An existing ground filtering approach is modified to handle variations in point density of mobile LiDAR data. Hydrological analyses, including flow direction and flow accumulation, are applied to extract the drainage network from the digital terrain model (DTM). Cross-sectional/longitudinal profiles of the ditch are automatically extracted from LiDAR data, and visualized in 3D point clouds and 2D images. The slope derived from the LiDAR data was found to be very close to highway cross slope design standards of 2% on driving lanes, 4% on shoulder, as well as 6-by-1 slope for ditch lines. Potential flooded regions are identified by detecting areas with no LiDAR return and a recall score of 54% and 92% was achieved by the medium-grade wheel-based and vehicle-mounted portable systems, respectively.

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Ennasr, Osama, Michael Paquette, and Garry Glaspell. UGV SLAM payload for low-visibility environments. Engineer Research and Development Center (U.S.), September 2023. http://dx.doi.org/10.21079/11681/47589.

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Herein, we explore using a low size, weight, power, and cost unmanned ground vehicle payload designed specifically for low-visibility environments. The proposed payload simultaneously localizes and maps in GPS-denied environments via waypoint navigation. This solution utilizes a diverse sensor payload that includes wheel encoders, inertial measurement unit, 3D lidar, 3D ultrasonic sensors, and thermal cameras. Furthermore, the resulting 3D point cloud was compared against a survey-grade lidar.

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Ennasr, Osama, Charles Ellison, Anton Netchaev, Ahmet Soylemezoglu, and Garry Glaspell. Unmanned ground vehicle (UGV) path planning in 2.5D and 3D. Engineer Research and Development Center (U.S.), August 2023. http://dx.doi.org/10.21079/11681/47459.

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Herein, we explored path planning in 2.5D and 3D for unmanned ground vehicle (UGV) applications. For real-time 2.5D navigation, we investigated generating 2.5D occupancy grids using either elevation or traversability to determine path costs. Compared to elevation, traversability, which used a layered approach generated from surface normals, was more robust for the tested environments. A layered approached was also used for 3D path planning. While it was possible to use the 3D approach in real time, the time required to generate 3D meshes meant that the only way to effectively path plan was to use a preexisting point cloud environment. As a result, we explored generating 3D meshes from a variety of sources, including handheld sensors, UGVs, UAVs, and aerial lidar.

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Blundell, S., and Philip Devine. Creation, transformation, and orientation adjustment of a building façade model for feature segmentation : transforming 3D building point cloud models into 2D georeferenced feature overlays. Engineer Research and Development Center (U.S.), January 2020. http://dx.doi.org/10.21079/11681/35115.

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