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

Author: Grafiati

Published: 1 June 2024

Last updated: 31 July 2025

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1

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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Decker, Kevin T., and Brett J. Borghetti. "Hyperspectral Point Cloud Projection for the Semantic Segmentation of Multimodal Hyperspectral and Lidar Data with Point Convolution-Based Deep Fusion Neural Networks." Applied Sciences 13, no. 14 (2023): 8210. http://dx.doi.org/10.3390/app13148210.

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The fusion of dissimilar data modalities in neural networks presents a significant challenge, particularly in the case of multimodal hyperspectral and lidar data. Hyperspectral data, typically represented as images with potentially hundreds of bands, provide a wealth of spectral information, while lidar data, commonly represented as point clouds with millions of unordered points in 3D space, offer structural information. The complementary nature of these data types presents a unique challenge due to their fundamentally different representations requiring distinct processing methods. In this wo

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Lin, Yu, Yigong Wang, Yi-Fan Li, Zhuoyi Wang, Yang Gao, and Latifur Khan. "Single View Point Cloud Generation via Unified 3D Prototype." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 3 (2021): 2064–72. http://dx.doi.org/10.1609/aaai.v35i3.16303.

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As 3D point clouds become the representation of choice for multiple vision and graphics applications, such as autonomous driving, robotics, etc., the generation of them by deep neural networks has attracted increasing attention in the research community. Despite the recent success of deep learning models in classification and segmentation, synthesizing point clouds remains challenging, especially from a single image. State-of-the-art (SOTA) approaches can generate a point cloud from a hidden vector, however, they treat 2D and 3D features equally and disregard the rich shape information within

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Wang, Tiansheng. "PG-Net:3D point cloud completion based on graph convolutional network." Applied and Computational Engineering 13, no. 1 (2023): 189–98. http://dx.doi.org/10.54254/2755-2721/13/20230731.

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With the advancement of autonomous driving technology, the problem of 3D point cloud completion has become increasingly important. Completing 3D point clouds can improve the accuracy of 3D object detection, which is crucial for the development of autonomous driving and other related fields. In this paper, we propose a new approach for 3D point cloud completion tasks using point cloud representation. We focuses on the point cloud completion problem using Graph Neural Network methods, which are known for their ability to capture topological features. Our approach utilizes key components extracti

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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 (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 ar

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Wang, Yang, and Shunping Xiao. "Affinity-Point Graph Convolutional Network for 3D Point Cloud Analysis." Applied Sciences 12, no. 11 (2022): 5328. http://dx.doi.org/10.3390/app12115328.

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Efficient learning of 3D shape representation from point cloud is one of the biggest requirements in 3D computer vision. In recent years, convolutional neural networks have achieved great success in 2D image representation learning. However, unlike images that have a Euclidean structure, 3D point clouds are irregular since the neighbors of each node are inconsistent. Many studies have tried to develop various convolutional graph neural networks to overcome this problem and to achieve great results. Nevertheless, these studies simply took the centroid point and its corresponding neighbors as th

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Yang, Xi, Mengqing Cao, Cong Li, Hua Zhao, and Dong Yang. "Learning Implicit Neural Representation for Satellite Object Mesh Reconstruction." Remote Sensing 15, no. 17 (2023): 4163. http://dx.doi.org/10.3390/rs15174163.

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Constructing a surface representation from the sparse point cloud of a satellite is an important task for satellite on-orbit services such as satellite docking and maintenance. In related studies on surface reconstruction from point clouds, implicit neural representations have gained popularity in learning-based 3D object reconstruction. When aiming for a satellite with a more complicated geometry and larger intra-class variance, existing implicit approaches cannot perform well. To solve the above contradictions and make effective use of implicit neural representations, we built a NASA3D datas

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Zhang, Le, Jian Sun, and Qiang Zheng. "3D Point Cloud Recognition Based on a Multi-View Convolutional Neural Network." Sensors 18, no. 11 (2018): 3681. http://dx.doi.org/10.3390/s18113681.

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The recognition of three-dimensional (3D) lidar (light detection and ranging) point clouds remains a significant issue in point cloud processing. Traditional point cloud recognition employs the 3D point clouds from the whole object. Nevertheless, the lidar data is a collection of two-and-a-half-dimensional (2.5D) point clouds (each 2.5D point cloud comes from a single view) obtained by scanning the object within a certain field angle by lidar. To deal with this problem, we initially propose a novel representation which expresses 3D point clouds using 2.5D point clouds from multiple views and t

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Tohidi, Faranak, Manoranjan Paul, Anwaar Ulhaq, and Subrata Chakraborty. "Improved Video-Based Point Cloud Compression via Segmentation." Sensors 24, no. 13 (2024): 4285. http://dx.doi.org/10.3390/s24134285.

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A point cloud is a representation of objects or scenes utilising unordered points comprising 3D positions and attributes. The ability of point clouds to mimic natural forms has gained significant attention from diverse applied fields, such as virtual reality and augmented reality. However, the point cloud, especially those representing dynamic scenes or objects in motion, must be compressed efficiently due to its huge data volume. The latest video-based point cloud compression (V-PCC) standard for dynamic point clouds divides the 3D point cloud into many patches using computationally expensive

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Fan, Xiangsuo, Dachuan Xiao, Dengsheng Cai, and Wentao Ding. "Real Pseudo-Lidar Point Cloud Fusion for 3D Object Detection." Electronics 12, no. 18 (2023): 3920. http://dx.doi.org/10.3390/electronics12183920.

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Three-dimensional object detection technology is an essential component of autonomous driving systems. Existing 3D object detection techniques heavily rely on expensive lidar sensors, leading to increased costs. Recently, the emergence of Pseudo-Lidar point cloud data has addressed this cost issue. However, the current methods for generating Pseudo-Lidar point clouds are relatively crude, resulting in suboptimal detection performance. This paper proposes an improved method to generate more accurate Pseudo-Lidar point clouds. The method first enhances the stereo-matching network to improve the

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Liu, Shaolei, Kexue Fu, Manning Wang, and Zhijian Song. "Group-in-Group Relation-Based Transformer for 3D Point Cloud Learning." Remote Sensing 14, no. 7 (2022): 1563. http://dx.doi.org/10.3390/rs14071563.

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Deep point cloud neural networks have achieved promising performance in remote sensing applications, and the prevalence of Transformer in natural language processing and computer vision is in stark contrast to underexplored point-based methods. In this paper, we propose an effective transformer-based network for point cloud learning. To better learn global and local information, we propose a group-in-group relation-based transformer architecture to learn the relationships between point groups to model global information and between points within each group to model local semantic information.

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Liu, Weiping, Jia Sun, Wanyi Li, Ting Hu, and Peng Wang. "Deep Learning on Point Clouds and Its Application: A Survey." Sensors 19, no. 19 (2019): 4188. http://dx.doi.org/10.3390/s19194188.

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Point cloud is a widely used 3D data form, which can be produced by depth sensors, such as Light Detection and Ranging (LIDAR) and RGB-D cameras. Being unordered and irregular, many researchers focused on the feature engineering of the point cloud. Being able to learn complex hierarchical structures, deep learning has achieved great success with images from cameras. Recently, many researchers have adapted it into the applications of the point cloud. In this paper, the recent existing point cloud feature learning methods are classified as point-based and tree-based. The former directly takes th

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Hairuddin, A., S. Azri, U. Ujang, M. G. Cuétara, G. M. Retortillo, and S. Mohd Salleh. "DEVELOPMENT OF 3D CITY MODEL USING VIDEOGRAMMETRY TECHNIQUE." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-4/W16 (October 1, 2019): 221–28. http://dx.doi.org/10.5194/isprs-archives-xlii-4-w16-221-2019.

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Abstract. 3D city model is a representation of urban area in digital format that contains building and other information. The current approaches are using photogrammetry and laser scanning to develop 3D city model. However, these techniques are time consuming and quite costly. Besides that, laser scanning and photogrammetry need professional skills and expertise to handle hardware and tools. In this study, videogrammetry is proposed as a technique to develop 3D city model. This technique uses video frame sequences to generate point cloud. Videos are processed using EyesCloud3D by eCapture. Eye

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El Sayed, Abdul Rahman, Abdallah El Chakik, Hassan Alabboud, and Adnan Yassine. "An efficient simplification method for point cloud based on salient regions detection." RAIRO - Operations Research 53, no. 2 (2019): 487–504. http://dx.doi.org/10.1051/ro/2018082.

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Many computer vision approaches for point clouds processing consider 3D simplification as an important preprocessing phase. On the other hand, the big amount of point cloud data that describe a 3D object require excessively a large storage and long processing time. In this paper, we present an efficient simplification method for 3D point clouds using weighted graphs representation that optimizes the point clouds and maintain the characteristics of the initial data. This method detects the features regions that describe the geometry of the surface. These features regions are detected using the

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Xu, Mutian, Junhao Zhang, Zhipeng Zhou, Mingye Xu, Xiaojuan Qi, and Yu Qiao. "Learning Geometry-Disentangled Representation for Complementary Understanding of 3D Object Point Cloud." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 4 (2021): 3056–64. http://dx.doi.org/10.1609/aaai.v35i4.16414.

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In 2D image processing, some attempts decompose images into high and low frequency components for describing edge and smooth parts respectively. Similarly, the contour and flat area of 3D objects, such as the boundary and seat area of a chair, describe different but also complementary geometries. However, such investigation is lost in previous deep networks that understand point clouds by directly treating all points or local patches equally. To solve this problem, we propose Geometry-Disentangled Attention Network (GDANet). GDANet introduces Geometry-Disentangle Module to dynamically disentan

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Atik, Muhammed Enes, and Zaide Duran. "An Efficient Ensemble Deep Learning Approach for Semantic Point Cloud Segmentation Based on 3D Geometric Features and Range Images." Sensors 22, no. 16 (2022): 6210. http://dx.doi.org/10.3390/s22166210.

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Mobile light detection and ranging (LiDAR) sensor point clouds are used in many fields such as road network management, architecture and urban planning, and 3D High Definition (HD) city maps for autonomous vehicles. Semantic segmentation of mobile point clouds is critical for these tasks. In this study, we present a robust and effective deep learning-based point cloud semantic segmentation method. Semantic segmentation is applied to range images produced from point cloud with spherical projection. Irregular 3D mobile point clouds are transformed into regular form by projecting the clouds onto

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Chu, Xutao, Shengjie Zhao, and Hongwei Dai. "AIFormer: Adaptive Interaction Transformer for 3D Point Cloud Understanding." Remote Sensing 16, no. 21 (2024): 4103. http://dx.doi.org/10.3390/rs16214103.

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Recently, significant advancements have been made in 3D point cloud analysis by leveraging transformer architecture in 3D space. However, it remains challenging to effectively implement local and global learning within irregular and sparse structures of 3D point clouds. This paper presents the Adaptive Interaction Transformer (AIFormer), a novel hierarchical transformer architecture designed to enhance 3D point cloud analysis by fusing local and global features through the adaptive interaction of features. Specifically, AIFormer mainly consists of several stacked AIFormer Blocks. Each AIFormer

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Suriyababu, Vijai Kumar, Cornelis Vuik, and Matthias Möller. "Resampling Point Clouds Using Series of Local Triangulations." Journal of Imaging 11, no. 2 (2025): 49. https://doi.org/10.3390/jimaging11020049.

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The increasing reliance on 3D scanning and meshless methods highlights the need for algorithms optimized for point-cloud geometry representations in CAE simulations. While voxel-based binning methods are simple, they often compromise geometry and topology, particularly with coarse voxelizations. We propose an algorithm based on a Series of Local Triangulations (SOLT) as an intermediate representation for point clouds, enabling efficient upsampling and downsampling. This robust and straightforward approach preserves the integrity of point clouds, ensuring resampling without feature loss or topo

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Ma, Wuwei, Xi Yang, Qiufeng Wang, Kaizhu Huang, and Xiaowei Huang. "Multi-Scope Feature Extraction for Intracranial Aneurysm 3D Point Cloud Completion." Cells 11, no. 24 (2022): 4107. http://dx.doi.org/10.3390/cells11244107.

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3D point clouds are gradually becoming more widely used in the medical field, however, they are rarely used for 3D representation of intracranial vessels and aneurysms due to the time-consuming data reconstruction. In this paper, we simulate the incomplete intracranial vessels (including aneurysms) in the actual collection from different angles, then propose Multi-Scope Feature Extraction Network (MSENet) for Intracranial Aneurysm 3D Point Cloud Completion. MSENet adopts a multi-scope feature extraction encoder to extract the global features from the incomplete point cloud. This encoder utiliz

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Laupheimer, D., M. H. Shams Eddin, and N. Haala. "ON THE ASSOCIATION OF LIDAR POINT CLOUDS AND TEXTURED MESHES FOR MULTI-MODAL SEMANTIC SEGMENTATION." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences V-2-2020 (August 3, 2020): 509–16. http://dx.doi.org/10.5194/isprs-annals-v-2-2020-509-2020.

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Abstract. The semantic segmentation of the huge amount of acquired 3D data has become an important task in recent years. We propose a novel association mechanism that enables information transfer between two 3D representations: point clouds and meshes. The association mechanism can be used in a two-fold manner: (i) feature transfer to stabilize semantic segmentation of one representation with features from the other representation and (ii) label transfer to achieve the semantic annotation of both representations. We claim that point clouds are an intermediate product whereas meshes are a final

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Zhu, Feng, Jieyu Zhao, and Zhengyi Cai. "A Contrastive Learning Method for the Visual Representation of 3D Point Clouds." Algorithms 15, no. 3 (2022): 89. http://dx.doi.org/10.3390/a15030089.

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At present, the unsupervised visual representation learning of the point cloud model is mainly based on generative methods, but the generative methods pay too much attention to the details of each point, thus ignoring the learning of semantic information. Therefore, this paper proposes a discriminative method for the contrastive learning of three-dimensional point cloud visual representations, which can effectively learn the visual representation of point cloud models. The self-attention point cloud capsule network is designed as the backbone network, which can effectively extract the features

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Zhang, Jingwen, Zikun Zhou, Guangming Lu, Jiandong Tian, and Wenjie Pei. "Robust 3D Tracking with Quality-Aware Shape Completion." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 7 (2024): 7160–68. http://dx.doi.org/10.1609/aaai.v38i7.28544.

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3D single object tracking remains a challenging problem due to the sparsity and incompleteness of the point clouds. Existing algorithms attempt to address the challenges in two strategies. The first strategy is to learn dense geometric features based on the captured sparse point cloud. Nevertheless, it is quite a formidable task since the learned dense geometric features are with high uncertainty for depicting the shape of the target object. The other strategy is to aggregate the sparse geometric features of multiple templates to enrich the shape information, which is a routine solution in 2D

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Markiewicz, J. S., Ł. Markiewicz, and P. Foryś. "THE COMPARISON OF 2D AND 3D DETECTORS FOR TLS DATA REGISTRATION – PRELIMINARY RESULTS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W9 (January 31, 2019): 467–72. http://dx.doi.org/10.5194/isprs-archives-xlii-2-w9-467-2019.

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<p><strong>Abstract.</strong> This paper presents the analysis of possible methods of a terrestrial laser scanning (TLS) data registration using 2D/3D detectors and descriptors. The developed approach, where point clouds are processed in form of panoramic images, orthoimages and 3D data, was described. The accuracy of the registration process was preliminary verified. The two approaches were analysed and compared: the 2D SIFT (Scale-Invariant Feature Transform) detector and descriptor with the rasterized TLS data and the 3D SIFT detector with the 3D FPFH (Fast Point Feature H

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Xu, Ronghua, Yu Chen, Genshe Chen, and Erik Blasch. "SAUSA: Securing Access, Usage, and Storage of 3D Point CloudData by a Blockchain-Based Authentication Network." Future Internet 14, no. 12 (2022): 354. http://dx.doi.org/10.3390/fi14120354.

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The rapid development of three-dimensional (3D) acquisition technology based on 3D sensors provides a large volume of data, which are often represented in the form of point clouds. Point cloud representation can preserve the original geometric information along with associated attributes in a 3D space. Therefore, it has been widely adopted in many scene-understanding-related applications such as virtual reality (VR) and autonomous driving. However, the massive amount of point cloud data aggregated from distributed 3D sensors also poses challenges for secure data collection, management, storage

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Huang, Xiaoshui, Zhou Huang, Sheng Li, et al. "Frozen CLIP Transformer Is an Efficient Point Cloud Encoder." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 3 (2024): 2382–90. http://dx.doi.org/10.1609/aaai.v38i3.28013.

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The pretrain-finetune paradigm has achieved great success in NLP and 2D image fields because of the high-quality representation ability and transferability of their pretrained models. However, pretraining such a strong model is difficult in the 3D point cloud field due to the limited amount of point cloud sequences. This paper introduces Efficient Point Cloud Learning (EPCL), an effective and efficient point cloud learner for directly training high-quality point cloud models with a frozen CLIP transformer. Our EPCL connects the 2D and 3D modalities by semantically aligning the image features a

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Huang, Rui, Xuran Pan, Henry Zheng, et al. "Joint representation learning for text and 3D point cloud." Pattern Recognition 147 (March 2024): 110086. http://dx.doi.org/10.1016/j.patcog.2023.110086.

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You, Haoxuan, Yifan Feng, Xibin Zhao, Changqing Zou, Rongrong Ji, and Yue Gao. "PVRNet: Point-View Relation Neural Network for 3D Shape Recognition." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 9119–26. http://dx.doi.org/10.1609/aaai.v33i01.33019119.

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Three-dimensional (3D) shape recognition has drawn much research attention in the field of computer vision. The advances of deep learning encourage various deep models for 3D feature representation. For point cloud and multi-view data, two popular 3D data modalities, different models are proposed with remarkable performance. However the relation between point cloud and views has been rarely investigated. In this paper, we introduce Point-View Relation Network (PVRNet), an effective network designed to well fuse the view features and the point cloud feature with a proposed relation score module

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Poux, F., R. Neuville, P. Hallot, and R. Billen. "MODEL FOR SEMANTICALLY RICH POINT CLOUD DATA." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences IV-4/W5 (October 23, 2017): 107–15. http://dx.doi.org/10.5194/isprs-annals-iv-4-w5-107-2017.

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This paper proposes an interoperable model for managing high dimensional point clouds while integrating semantics. Point clouds from sensors are a direct source of information physically describing a 3D state of the recorded environment. As such, they are an exhaustive representation of the real world at every scale: 3D reality-based spatial data. Their generation is increasingly fast but processing routines and data models lack of knowledge to reason from information extraction rather than interpretation. The enhanced smart point cloud developed model allows to bring intelligence to point clo

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Liu, Huaijin, Jixiang Du, Yong Zhang, and Hongbo Zhang. "Enhancing Point Features with Spatial Information for Point-Based 3D Object Detection." Scientific Programming 2021 (December 21, 2021): 1–11. http://dx.doi.org/10.1155/2021/4650660.

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Currently, there are many kinds of voxel-based multisensor 3D object detectors, while point-based multisensor 3D object detectors have not been fully studied. In this paper, we propose a new 3D two-stage object detection method based on point cloud and image fusion to improve the detection accuracy. To address the problem of insufficient semantic information of point cloud, we perform multiscale deep fusion of LiDAR point and camera image in a point-wise manner to enhance point features. Due to the imbalance of LiDAR points, the object point cloud in the long-distance area is sparse. We design

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Yu, Siyang, Si Sun, Wei Yan, Guangshuai Liu, and Xurui Li. "A Method Based on Curvature and Hierarchical Strategy for Dynamic Point Cloud Compression in Augmented and Virtual Reality System." Sensors 22, no. 3 (2022): 1262. http://dx.doi.org/10.3390/s22031262.

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As a kind of information-intensive 3D representation, point cloud rapidly develops in immersive applications, which has also sparked new attention in point cloud compression. The most popular dynamic methods ignore the characteristics of point clouds and use an exhaustive neighborhood search, which seriously impacts the encoder’s runtime. Therefore, we propose an improved compression means for dynamic point cloud based on curvature estimation and hierarchical strategy to meet the demands in real-world scenarios. This method includes initial segmentation derived from the similarity between norm

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Ding, Ziheng, Xiaze Zhang, Qi Jing, Ying Cheng, and Rui Feng. "AS-Det: Active Sampling for Adaptive 3D Object Detection in Point Clouds." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 3 (2025): 2762–70. https://doi.org/10.1609/aaai.v39i3.32281.

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3D object detection in point clouds is critical in 3D computer vision, autonomous driving, and robotics. Existing point-based detectors, tailored to handle unstructured raw point clouds, often rely on simplistic sampling strategies to select a subset of points for local representation learning and detection. However, the diverse patterns exhibited by multiple types of point cloud data present a significant challenge to the universality of current detectors, particularly those captured by varied sensors (e.g., LiDAR and 4D Imaging Radar). In response to this challenge, we introduce an adaptable

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Hoang, Long, Suk-Hwan Lee, Eung-Joo Lee, and Ki-Ryong Kwon. "GSV-NET: A Multi-Modal Deep Learning Network for 3D Point Cloud Classification." Applied Sciences 12, no. 1 (2022): 483. http://dx.doi.org/10.3390/app12010483.

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Light Detection and Ranging (LiDAR), which applies light in the formation of a pulsed laser to estimate the distance between the LiDAR sensor and objects, is an effective remote sensing technology. Many applications use LiDAR including autonomous vehicles, robotics, and virtual and augmented reality (VR/AR). The 3D point cloud classification is now a hot research topic with the evolution of LiDAR technology. This research aims to provide a high performance and compatible real-world data method for 3D point cloud classification. More specifically, we introduce a novel framework for 3D point clo

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Huang, Ming, Xueyu Wu, Xianglei Liu, Tianhang Meng, and Peiyuan Zhu. "Integration of Constructive Solid Geometry and Boundary Representation (CSG-BRep) for 3D Modeling of Underground Cable Wells from Point Clouds." Remote Sensing 12, no. 9 (2020): 1452. http://dx.doi.org/10.3390/rs12091452.

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The preference of three-dimensional representation of underground cable wells from two-dimensional symbols is a developing trend, and three-dimensional (3D) point cloud data is widely used due to its high precision. In this study, we utilize the characteristics of 3D terrestrial lidar point cloud data to build a CSG-BRep 3D model of underground cable wells, whose spatial topological relationship is fully considered. In order to simplify the modeling process, first, point cloud simplification is performed; then, the point cloud main axis is extracted by OBB bounding box, and lastly the point cl

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Chen, Shuaijun, Jinxi Wang, Wei Pan, Shang Gao, Meili Wang, and Xuequan Lu. "Towards uniform point distribution in feature-preserving point cloud filtering." Computational Visual Media 9, no. 2 (2023): 249–63. http://dx.doi.org/10.1007/s41095-022-0278-4.

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AbstractWhile a popular representation of 3D data, point clouds may contain noise and need filtering before use. Existing point cloud filtering methods either cannot preserve sharp features or result in uneven point distributions in the filtered output. To address this problem, this paper introduces a point cloud filtering method that considers both point distribution and feature preservation during filtering. The key idea is to incorporate a repulsion term with a data term in energy minimization. The repulsion term is responsible for the point distribution, while the data term aims to approxi

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Li, Wenrui, Wei Han, Yandu Chen, et al. "Riemann-based Multi-scale Attention Reasoning Network for Text-3D Retrieval." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 17 (2025): 18485–93. https://doi.org/10.1609/aaai.v39i17.34034.

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Due to the challenges in acquiring paired Text-3D data and the inherent irregularity of 3D data structures, combined representation learning of 3D point clouds and text remains unexplored. In this paper, we propose a novel Riemann-based Multi-scale Attention Reasoning Network (RMARN) for text-3D retrieval. Specifically, the extracted text and point cloud features are refined by their respective Adaptive Feature Refiner (AFR). Furthermore, we introduce the innovative Riemann Local Similarity (RLS) module and the Global Pooling Similarity (GPS) module. However, as 3D point cloud data and text da

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Poliyapram, Vinayaraj, Weimin Wang, and Ryosuke Nakamura. "A Point-Wise LiDAR and Image Multimodal Fusion Network (PMNet) for Aerial Point Cloud 3D Semantic Segmentation." Remote Sensing 11, no. 24 (2019): 2961. http://dx.doi.org/10.3390/rs11242961.

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3D semantic segmentation of point cloud aims at assigning semantic labels to each point by utilizing and respecting the 3D representation of the data. Detailed 3D semantic segmentation of urban areas can assist policymakers, insurance companies, governmental agencies for applications such as urban growth assessment, disaster management, and traffic supervision. The recent proliferation of remote sensing techniques has led to producing high resolution multimodal geospatial data. Nonetheless, currently, only limited technologies are available to fuse the multimodal dataset effectively. Therefore

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Firintepe, Ahmet, Carolin Vey, Stylianos Asteriadis, Alain Pagani, and Didier Stricker. "From IR Images to Point Clouds to Pose: Point Cloud-Based AR Glasses Pose Estimation." Journal of Imaging 7, no. 5 (2021): 80. http://dx.doi.org/10.3390/jimaging7050080.

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In this paper, we propose two novel AR glasses pose estimation algorithms from single infrared images by using 3D point clouds as an intermediate representation. Our first approach “PointsToRotation” is based on a Deep Neural Network alone, whereas our second approach “PointsToPose” is a hybrid model combining Deep Learning and a voting-based mechanism. Our methods utilize a point cloud estimator, which we trained on multi-view infrared images in a semi-supervised manner, generating point clouds based on one image only. We generate a point cloud dataset with our point cloud estimator using the

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Leal, Esmeide, German Sanchez-Torres, John W. Branch-Bedoya, Francisco Abad, and Nallig Leal. "A Saliency-Based Sparse Representation Method for Point Cloud Simplification." Sensors 21, no. 13 (2021): 4279. http://dx.doi.org/10.3390/s21134279.

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High-resolution 3D scanning devices produce high-density point clouds, which require a large capacity of storage and time-consuming processing algorithms. In order to reduce both needs, it is common to apply surface simplification algorithms as a preprocessing stage. The goal of point cloud simplification algorithms is to reduce the volume of data while preserving the most relevant features of the original point cloud. In this paper, we present a new point cloud feature-preserving simplification algorithm. We use a global approach to detect saliencies on a given point cloud. Our method estimat

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Du, Han, Benhe Cai, Xiaoming Li, Weixi Wang, and Shengjun Tang. "Method for Generating Indoor 3D Scene Graphs Based on Instance Features and Relationship Encoding." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVIII-1-2024 (May 10, 2024): 135–40. http://dx.doi.org/10.5194/isprs-archives-xlviii-1-2024-135-2024.

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Abstract. A 3D scene graph is a compact and explicit representation in scene analysis. In today’s 3D scene graph prediction methods, the feature encoding method of nodes and edges is relatively simple, which essentially hinders the network from fully learning 3D point cloud features. In this paper, we propose a 3D scene graph task framework that fully expresses node and edge features, trying to meet the requirements of fully utilizing point cloud features to achieve high-precision prediction. Experimental results show that with the help of the new representation method, the prediction performa

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Lin, Guoting, Zexun Zheng, Lin Chen, Tianyi Qin, and Jiahui Song. "Multi-Modal 3D Shape Clustering with Dual Contrastive Learning." Applied Sciences 12, no. 15 (2022): 7384. http://dx.doi.org/10.3390/app12157384.

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3D shape clustering is developing into an important research subject with the wide applications of 3D shapes in computer vision and multimedia fields. Since 3D shapes generally take on various modalities, how to comprehensively exploit the multi-modal properties to boost clustering performance has become a key issue for the 3D shape clustering task. Taking into account the advantages of multiple views and point clouds, this paper proposes the first multi-modal 3D shape clustering method, named the dual contrastive learning network (DCL-Net), to discover the clustering partitions of unlabeled 3

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Razali, A. F., M. F. M. Ariff, and Z. Majid. "A HYBRID POINT CLOUD REALITY CAPTURE FROM TERRESTRIAL LASER SCANNING AND UAV-PHOTOGRAMMETRY." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVI-2/W1-2022 (February 25, 2022): 459–63. http://dx.doi.org/10.5194/isprs-archives-xlvi-2-w1-2022-459-2022.

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Abstract. Point clouds are a digital representation of physical objects or buildings that exist in real world. There are many sources that a point cloud can come from such as a terrestrial laser scanner (TLS) or an unmanned aerial vehicle (UAV). This paper presents a simple method of integrating point clouds from two (2) data sources; TLS and UAV using simple alignment of rigid body transformation method known as Point Pair Picking (PPP). The point cloud data are the representation of details of a one-story building located in Johor Bahru, Malaysia. The process of aligning two (2) separate clo

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