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Relevant bibliographies by topics / 3D point cloud representation

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Academic literature on the topic '3D point cloud representation'

Author: Grafiati

Published: 1 June 2024

Last updated: 31 July 2025

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Journal articles on the topic "3D point cloud representation"

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Arya, Hemlata, Parul Saxena, and Jaimala Jha. "Detection of 3D Object in Point Cloud: Cloud Semantic Segmentation in Lane Marking." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 10s (2023): 376–81. http://dx.doi.org/10.17762/ijritcc.v11i10s.7645.

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Managing a city efficiently and effectively is more important than ever as growing population and economic strain put a strain on infrastructure like transportation and public services like keeping urban green areas clean and maintained. For effective administration, knowledge of the urban setting is essential. Both portable and stationary laser scanners generate 3D point clouds that accurately depict the environment. These data points may be used to infer the state of the roads, buildings, trees, and other important elements involved in this decision-making process. Perhaps they would support

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Barnefske, E., and H. Sternberg. "PCCT: A POINT CLOUD CLASSIFICATION TOOL TO CREATE 3D TRAINING DATA TO ADJUST AND DEVELOP 3D CONVNET." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W16 (September 17, 2019): 35–40. http://dx.doi.org/10.5194/isprs-archives-xlii-2-w16-35-2019.

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<p><strong>Abstract.</strong> Point clouds give a very detailed and sometimes very accurate representation of the geometry of captured objects. In surveying, point clouds captured with laser scanners or camera systems are an intermediate result that must be processed further. Often the point cloud has to be divided into regions of similar types (object classes) for the next process steps. These classifications are very time-consuming and cost-intensive compared to acquisition. In order to automate this process step, conventional neural networks (ConvNet), which take over the

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Wu, Qiuxia, Haiyang Huang, Kunming Su, Zhiyong Wang, and Kun Hu. "DC-PCN: Point Cloud Completion Network with Dual-Codebook Guided Quantization." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 8 (2025): 8441–49. https://doi.org/10.1609/aaai.v39i8.32911.

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Point cloud completion aims to reconstruct complete 3D shapes from partial 3D point clouds. With advancements in deep learning techniques, various methods for point cloud completion have been developed. Despite achieving encouraging results, a significant issue remains: these methods often overlook the variability in point clouds sampled from a single 3D object surface. This variability can lead to ambiguity and hinder the achievement of more precise completion results. Therefore, in this study, we introduce a novel point cloud completion network, namely Dual-Codebook Point Completion Network

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Orts-Escolano, Sergio, Jose Garcia-Rodriguez, Miguel Cazorla, et al. "Bioinspired point cloud representation: 3D object tracking." Neural Computing and Applications 29, no. 9 (2016): 663–72. http://dx.doi.org/10.1007/s00521-016-2585-0.

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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. De

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Chen, Hanyu, Bailey Miller, and Ioannis Gkioulekas. "3D Reconstruction with Fast Dipole Sums." ACM Transactions on Graphics 43, no. 6 (2024): 1–19. http://dx.doi.org/10.1145/3687914.

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We introduce a method for high-quality 3D reconstruction from multi-view images. Our method uses a new point-based representation, the regularized dipole sum, which generalizes the winding number to allow for interpolation of per-point attributes in point clouds with noisy or outlier points. Using regularized dipole sums, we represent implicit geometry and radiance fields as per-point attributes of a dense point cloud, which we initialize from structure from motion. We additionally derive Barnes-Hut fast summation schemes for accelerated forward and adjoint dipole sum queries. These queries fa

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Yang, Zexin, Qin Ye, Jantien Stoter, and Liangliang Nan. "Enriching Point Clouds with Implicit Representations for 3D Classification and Segmentation." Remote Sensing 15, no. 1 (2022): 61. http://dx.doi.org/10.3390/rs15010061.

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Continuous implicit representations can flexibly describe complex 3D geometry and offer excellent potential for 3D point cloud analysis. However, it remains challenging for existing point-based deep learning architectures to leverage the implicit representations due to the discrepancy in data structures between implicit fields and point clouds. In this work, we propose a new point cloud representation by integrating the 3D Cartesian coordinates with the intrinsic geometric information encapsulated in its implicit field. Specifically, we parameterize the continuous unsigned distance field aroun

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Sun, Yichen. "3D point cloud domain generalization via adversarial training." Applied and Computational Engineering 13, no. 1 (2023): 160–68. http://dx.doi.org/10.54254/2755-2721/13/20230725.

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The purpose of the paper is to tackle the classification problem of 3D point cloud data in domain generalization: how to develop a generalized feature representation for an unseen target domain by utilizing sub-field of numerous seen source domain(s). We present a novel methodology based on both adversarial training to learn a generalized feature representations across subdomains in domain adaptation called 3D-AA. We specifically expand adversarial autoencoders by applying the Maximum Mean Discrepancy (MMD) measure to align the distributions across several subdomains, and then matching the ali

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Quach, Maurice, Aladine Chetouani, Giuseppe Valenzise, and Frederic Dufaux. "A deep perceptual metric for 3D point clouds." Electronic Imaging 2021, no. 9 (2021): 257–1. http://dx.doi.org/10.2352/issn.2470-1173.2021.9.iqsp-257.

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Point clouds are essential for storage and transmission of 3D content. As they can entail significant volumes of data, point cloud compression is crucial for practical usage. Recently, point cloud geometry compression approaches based on deep neural networks have been explored. In this paper, we evaluate the ability to predict perceptual quality of typical voxel-based loss functions employed to train these networks. We find that the commonly used focal loss and weighted binary cross entropy are poorly correlated with human perception. We thus propose a perceptual loss function for 3D point clo

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Li, Shidi, Miaomiao Liu, and Christian Walder. "EditVAE: Unsupervised Parts-Aware Controllable 3D Point Cloud Shape Generation." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 2 (2022): 1386–94. http://dx.doi.org/10.1609/aaai.v36i2.20027.

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This paper tackles the problem of parts-aware point cloud generation. Unlike existing works which require the point cloud to be segmented into parts a priori, our parts-aware editing and generation are performed in an unsupervised manner. We achieve this with a simple modification of the Variational Auto-Encoder which yields a joint model of the point cloud itself along with a schematic representation of it as a combination of shape primitives. In particular, we introduce a latent representation of the point cloud which can be decomposed into a disentangled representation for each part of the

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Dissertations / Theses on the topic "3D point cloud representation"

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Diskin, Yakov. "Dense 3D Point Cloud Representation of a Scene Using Uncalibrated Monocular Vision." University of Dayton / OhioLINK, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1366386933.

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Diskin, Yakov. "Volumetric Change Detection Using Uncalibrated 3D Reconstruction Models." University of Dayton / OhioLINK, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1429293660.

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Morell, Vicente. "Contributions to 3D Data Registration and Representation." Doctoral thesis, Universidad de Alicante, 2014. http://hdl.handle.net/10045/42364.

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Nowadays, new computers generation provides a high performance that enables to build computationally expensive computer vision applications applied to mobile robotics. Building a map of the environment is a common task of a robot and is an essential part to allow the robots to move through these environments. Traditionally, mobile robots used a combination of several sensors from different technologies. Lasers, sonars and contact sensors have been typically used in any mobile robotic architecture, however color cameras are an important sensor due to we want the robots to use the same informati

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Orts-Escolano, Sergio. "A three-dimensional representation method for noisy point clouds based on growing self-organizing maps accelerated on GPUs." Doctoral thesis, Universidad de Alicante, 2013. http://hdl.handle.net/10045/36484.

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The research described in this thesis was motivated by the need of a robust model capable of representing 3D data obtained with 3D sensors, which are inherently noisy. In addition, time constraints have to be considered as these sensors are capable of providing a 3D data stream in real time. This thesis proposed the use of Self-Organizing Maps (SOMs) as a 3D representation model. In particular, we proposed the use of the Growing Neural Gas (GNG) network, which has been successfully used for clustering, pattern recognition and topology representation of multi-dimensional data. Until now, Self-O

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Zhao, Yongheng. "3D feature representations for visual perception and geometric shape understanding." Doctoral thesis, Università degli studi di Padova, 2019. http://hdl.handle.net/11577/3424787.

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In this thesis, we first present a unified look to several well known 3D feature representations, ranging from hand-crafted design to learning based ones. Then, we propose three kinds of feature representations from both RGB-D data and point cloud, addressing different problems and aiming for different functionality. With RGB-D data, we address the existing problems of 2D feature representation in visual perception by integrating with the 3D information. We propose an RGB-D data based feature representation which fuses object's statistical color model and depth information in a probabilisti

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Konradsson, Albin, and Gustav Bohman. "3D Instance Segmentation of Cluttered Scenes : A Comparative Study of 3D Data Representations." Thesis, Linköpings universitet, Datorseende, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-177598.

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This thesis provides a comparison between instance segmentation methods using point clouds and depth images. Specifically, their performance on cluttered scenes of irregular objects in an industrial environment is investigated. Recent work by Wang et al. [1] has suggested potential benefits of a point cloud representation when performing deep learning on data from 3D cameras. However, little work has been done to enable quantifiable comparisons between methods based on different representations, particularly on industrial data. Generating synthetic data provides accurate grayscale, depth map,

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Cao, Chao. "Compression d'objets 3D représentés par nuages de points." Electronic Thesis or Diss., Institut polytechnique de Paris, 2021. http://www.theses.fr/2021IPPAS015.

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Avec la croissance rapide du contenu multimédia, les objets 3D deviennent de plus en plus populaires. Ils sont généralement modélisés sous forme de maillages polygonaux complexes ou de nuages de points 3D denses, offrant des expériences immersives dans différentes applications multimédias industrielles et grand public. La représentation par nuages de points, plus facile à acquérir que les maillages, a suscité ces dernières année un intérêt croissant tant dans le monde académique que commercial. Un nuage de points est par définition un ensemble de points définissant la géométrie de l’objet et l

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Hejl, Zdeněk. "Rekonstrukce 3D scény z obrazových dat." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2012. http://www.nusl.cz/ntk/nusl-236495.

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This thesis describes methods of reconstruction of 3D scenes from photographs and videos using the Structure from motion approach. A new software capable of automatic reconstruction of point clouds and polygonal models from common images and videos was implemented based on these methods. The software uses variety of existing and custom solutions and clearly links them into one easily executable application. The reconstruction consists of feature point detection, pairwise matching, Bundle adjustment, stereoscopic algorithms and polygon model creation from point cloud using PCL library. Program

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Smith, Michael. "Non-parametric workspace modelling for mobile robots using push broom lasers." Thesis, University of Oxford, 2011. http://ora.ox.ac.uk/objects/uuid:50224eb9-73e8-4c8a-b8c5-18360d11e21b.

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This thesis is about the intelligent compression of large 3D point cloud datasets. The non-parametric method that we describe simultaneously generates a continuous representation of the workspace surfaces from discrete laser samples and decimates the dataset, retaining only locally salient samples. Our framework attains decimation factors in excess of two orders of magnitude without significant degradation in fidelity. The work presented here has a specific focus on gathering and processing laser measurements taken from a moving platform in outdoor workspaces. We introduce a somewhat unusual p

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Roure, Garcia Ferran. "Tools for 3D point cloud registration." Doctoral thesis, Universitat de Girona, 2017. http://hdl.handle.net/10803/403345.

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In this thesis, we did an in-depth review of the state of the art of 3D registration, evaluating the most popular methods. Given the lack of standardization in the literature, we also proposed a nomenclature and a classification to unify the evaluation systems and to be able to compare the different algorithms under the same criteria. The major contribution of the thesis is the Registration Toolbox, which consists of software and a database of 3D models. The software presented here consists of a 3D Registration Pipeline written in C ++ that allows researchers to try different methods, as we

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Books on the topic "3D point cloud representation"

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Liu, Shan, Min Zhang, Pranav Kadam, and C. C. Jay Kuo. 3D Point Cloud Analysis. 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. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-96110-7.

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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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3D Point Cloud Analysis: Traditional, Deep Learning, and Explainable Machine Learning Methods. Springer International Publishing AG, 2022.

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3D Point Cloud Analysis: Traditional, Deep Learning, and Explainable Machine Learning Methods. Springer International Publishing AG, 2021.

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Book chapters on the topic "3D point cloud representation"

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Szachniewicz, Michał, Wojciech Kozłowski, Michał Stypułkowski, and Maciej Zięba. "Self-supervised Adversarial Masking for 3D Point Cloud Representation Learning." In Intelligent Information and Database Systems. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-4985-0_13.

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Zdobylak, Adrian, and Maciej Zieba. "Semi-supervised Representation Learning for 3D Point Clouds." In Intelligent Information and Database Systems. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-41964-6_41.

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Liu, Jingya, Oguz Akin, and Yingli Tian. "Rethinking Pulmonary Nodule Detection in Multi-view 3D CT Point Cloud Representation." In Machine Learning in Medical Imaging. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-87589-3_9.

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Miyachi, Hideo, and Koshiro Murakami. "A Study of 3D Shape Similarity Search in Point Representation by Using Machine Learning." In Advances on P2P, Parallel, Grid, Cloud and Internet Computing. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-33509-0_24.

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Ćurković, Milan, and Damir Vučina. "Adaptive Representation of Large 3D Point Clouds for Shape Optimization." In Operations Research Proceedings. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-42902-1_74.

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He, Tong, Dong Gong, Zhi Tian, and Chunhua Shen. "Learning and Memorizing Representative Prototypes for 3D Point Cloud Semantic and Instance Segmentation." In Computer Vision – ECCV 2020. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-58523-5_33.

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Sant, Rohit, Ninad Kulkarni, Ainesh Bakshi, Salil Kapur, and Kratarth Goel. "Autonomous Robot Navigation: Path Planning on a Detail-Preserving Reduced-Complexity Representation of 3D Point Clouds." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-39402-7_18.

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Yang, Bisheng, Zhen Dong, Fuxun Liang, and Xiaoxin Mi. "3D Building Reconstruction." In Ubiquitous Point Cloud. CRC Press, 2024. http://dx.doi.org/10.1201/9781003486060-14.

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Yang, Bisheng, Zhen Dong, Fuxun Liang, and Xiaoxin Mi. "3D Road Reconstruction." In Ubiquitous Point Cloud. CRC Press, 2024. http://dx.doi.org/10.1201/9781003486060-15.

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Yang, Bisheng, Zhen Dong, Fuxun Liang, and Xiaoxin Mi. "3D Terrain Modeling." In Ubiquitous Point Cloud. CRC Press, 2024. http://dx.doi.org/10.1201/9781003486060-13.

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Conference papers on the topic "3D point cloud representation"

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Xing, Bowei, Xianghua Ying, and Ruibin Wang. "Masked Local-Global Representation Learning for 3D Point Cloud Domain Adaptation." In 2024 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2024. http://dx.doi.org/10.1109/icra57147.2024.10611402.

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Shin, Sangyun, Kaichen Zhou, Madhu Vankadari, Andrew Markham, and Niki Trigoni. "Spherical Mask: Coarse-to-Fine 3D Point Cloud Instance Segmentation with Spherical Representation." In 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2024. http://dx.doi.org/10.1109/cvpr52733.2024.00389.

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Rong, Yi, Haoran Zhou, Kang Xia, Cheng Mei, Jiahao Wang, and Tong Lu. "RepKPU: Point Cloud Upsampling with Kernel Point Representation and Deformation." In 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2024. http://dx.doi.org/10.1109/cvpr52733.2024.01989.

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Kim, Hye Rim, and Byoung Chul Ko. "Conditional Point Cloud Generation from Sketch Using Point-Voxel Representation." In 2025 International Conference on Electronics, Information, and Communication (ICEIC). IEEE, 2025. https://doi.org/10.1109/iceic64972.2025.10879614.

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Chujo, Yumeka, Yusuke Tagashira, Yukiko Harada, Kenji Kanai, and Jiro Katto. "Perceptual Quality Driven Point Cloud Compression for 6DoF 3D Point Cloud Streaming." In 2024 International Symposium on Multimedia (ISM). IEEE, 2024. https://doi.org/10.1109/ism63611.2024.00034.

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Eybposh, M. Hossein, Changjia Cai, Diptodip Deb, et al. "Computer-Generated Holography Using Point Cloud Processing Neural Networks." In 3D Image Acquisition and Display: Technology, Perception and Applications. Optica Publishing Group, 2023. http://dx.doi.org/10.1364/3d.2023.dw5a.4.

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We present a new deep-learning-based method for Computer Generated Holography (CGH) with point cloud representation. Our technique, DeepCGH2.0, dramatically reduces the size of the target image representations and synthesizes holograms in less than 2 milliseconds.

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Wang, Lihui, Jing Chen, and Baozong Yuan. "Simplified representation for 3D point cloud data." In 2010 10th International Conference on Signal Processing (ICSP 2010). IEEE, 2010. http://dx.doi.org/10.1109/icosp.2010.5656972.

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Li, Zongmin, Yupeng Zhang, and Yun Bai. "Geometric Invariant Representation Learning for 3D Point Cloud." In 2021 IEEE 33rd International Conference on Tools with Artificial Intelligence (ICTAI). IEEE, 2021. http://dx.doi.org/10.1109/ictai52525.2021.00235.

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Feng, Tuo, Wenguan Wang, Xiaohan Wang, Yi Yang, and Qinghua Zheng. "Clustering based Point Cloud Representation Learning for 3D Analysis." In 2023 IEEE/CVF International Conference on Computer Vision (ICCV). IEEE, 2023. http://dx.doi.org/10.1109/iccv51070.2023.00761.

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Su, Zhuo, Max Welling, Matti Pietikainen, and Li Liu. "SVNet: Where SO(3) Equivariance Meets Binarization on Point Cloud Representation." In 2022 International Conference on 3D Vision (3DV). IEEE, 2022. http://dx.doi.org/10.1109/3dv57658.2022.00084.

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Reports on the topic "3D point cloud representation"

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Smith, Curtis L., Steven Prescott, Kellie Kvarfordt, Ram Sampath, and Katie Larson. Status of the phenomena representation, 3D modeling, and cloud-based software architecture development. Office of Scientific and Technical Information (OSTI), 2015. http://dx.doi.org/10.2172/1245516.

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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.), 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.), 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

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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.), 2020. http://dx.doi.org/10.21079/11681/35115.

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Shin, Sang-Yeop, Hanjin Kim, Arnav Goel, Jinha Jung, and Ayman Habib. Development of Web Portal for the Management, Visualization, and Analysis of Collected Mobile LiDAR Data along Indiana’s Transportation Corridors. Purdue University, 2025. https://doi.org/10.5703/1288284317846.

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Efficient road management requires collecting and analyzing various geospatial data related to transportation corridors. Mobile Mapping Systems (MMS), integrated with GNSS/INS units, RGB cameras, and LiDAR technology, enable the collection of geo-tagged imagery and 3D point cloud data across roadway networks. Acquired data helps in the inventory and management of transportation networks. However, users face challenges in accessing high data volumes, especially when considering hardware and software requirements. To address these challenges, the Purdue research team developed a web portal to ef

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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 o

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