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A Survey of Point Cloud Completion

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Point cloud completion is able to estimate the complete point cloud starting from the missing point cloud, which obtains higher quality point cloud data for widely used in remote sensing 3-D modeling, medical imaging, robot vision, etc.
Zhiyun Zhuang, Zhiyang Zhi, Ting Han, Yiping Chen, Jun Chen, Cheng Wang, Ming Cheng, Xinchang Zhang, Nannan Qin, Lingfei Ma   +9 more
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Generative adversarial networks for high-fidelity 3D point cloud completion [PDF]

Scientific Reports
3D point clouds are essential for representing geometric structures in various fields such as autonomous driving and virtual reality. However, real-world data often suffers from incompleteness due to occlusions and noise, and existing completion methods ...
Di Zhao, Sizhe Mao, Junhan Shao, Hui Huang   +3 more
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Deep-learning-based point cloud completion methods: A review

Graphical Models
Point cloud completion aims to utilize algorithms to repair missing parts in 3D data for high-quality point clouds. This technology is crucial for applications such as autonomous driving and urban planning.
Kun Zhang, Ao Zhang, Xiaohong Wang, Weisong Li   +3 more
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FACNet: Feature alignment fast point cloud completion network

Computational Visual Media
Point cloud completion aims to infer complete point clouds based on partial 3D point cloud inputs. Various previous methods apply coarse-to-fine strategy networks for generating complete point clouds.
Xinxing Yu, Jianyi Li, Chi-Chong Wong, Chi-Man Vong, Yanyan Liang   +4 more
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HSPC-Net: A hierarchical shape-preserving completion network for machine part point cloud completion [PDF]

PLoS ONE
Yuchao Jiang, Honghui Fan, Hongjin Zhu
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Learning Contours for Point Cloud Completion

Remote Sensing, 2023
The integrity of a point cloud frequently suffers from discontinuous material surfaces or coarse sensor resolutions. Existing methods focus on reconstructing the overall structure, but salient points or small irregular surfaces are difficult to be ...
Jiabo Xu, Zeyun Wan, Jingbo Wei
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Data-Driven Point Cloud Objects Completion

Sensors, 2019
With the development of the laser scanning technique, it is easier to obtain 3D large-scale scene rapidly. However, many scanned objects may suffer serious incompletion caused by the scanning angles or occlusion, which has severely impacted their future ...
Yang Zhang, Zhen Liu, Xiang Li, Yu Zang
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Point Projection Network: A Multi-View-Based Point Completion Network with Encoder-Decoder Architecture

Remote Sensing, 2021
Recently, unstructured 3D point clouds have been widely used in remote sensing application. However, inevitable is the appearance of an incomplete point cloud, primarily due to the angle of view and blocking limitations. Therefore, point cloud completion
Weichao Wu, Zhong Xie, Yongyang Xu, Ziyin Zeng, Jie Wan   +4 more
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Vehicle Localization in a Completed City-Scale 3D Scene Using Aerial Images and an On-Board Stereo Camera

Remote Sensing, 2023
Simultaneous Localization and Mapping (SLAM) forms the foundation of vehicle localization in autonomous driving. Utilizing high-precision 3D scene maps as prior information in vehicle localization greatly assists in the navigation of autonomous vehicles ...
Haihan Zhang, Chun Xie, Hisatoshi Toriya, Hidehiko Shishido, Itaru Kitahara   +4 more
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Building-PCC: Building Point Cloud Completion Benchmarks [PDF]

ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
With the rapid advancement of 3D sensing technologies, obtaining 3D shape information of objects has become increasingly convenient. Lidar technology, with its capability to accurately capture the 3D information of objects at long distances, has been ...
W. Gao, R. Peters, J. Stoter
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