Results 11 to 20 of about 5,548,528 (274)

Data-Driven Point Cloud Objects Completion [PDF]

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
doaj   +4 more sources

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
doaj   +4 more sources

SPCNet: Stepwise Point Cloud Completion Network [PDF]

Computer Graphics Forum, 2022
AbstractHow will you repair a physical object with large missings? You may first recover its global yet coarse shape and stepwise increase its local details. We are motivated to imitate the above physical repair procedure to address the point cloud completion task.
Fei Hu, Honghua Chen, Xuequan Lu, Zhe Zhu, Jun Wang 0039, Weiming Wang 0002, Fu Lee Wang, Mingqiang Wei   +7 more
core   +6 more sources

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
doaj   +3 more sources

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
doaj   +3 more sources

Multistage Adaptive Point-Growth Network for Dense Point Cloud Completion

Remote Sensing, 2022
The point cloud data from actual measurements are often sparse and incomplete, making it difficult to apply them directly to visual processing and 3D reconstruction.
Ruidong Hao, Zhonghui Wei, Xu He, Kaifeng Zhu, Jun Wang, Jiawei He, Lei Zhang   +6 more
doaj   +2 more sources

A Partial Point Cloud Completion Network Focusing on Detail Reconstruction

Remote Sensing, 2023
The point cloud is disordered and unstructured, and it is difficult to extract detailed features. The detailed part of the target shape is difficult to complete in the point cloud completion task.
Ming Wei, Jiaqi Sun, Yaoyuan Zhang, Ming Zhu, Haitao Nie, Huiying Liu, Jiarong Wang   +6 more
doaj   +2 more sources

FBNet: Feedback Network for Point Cloud Completion [PDF]

, 2022
The rapid development of point cloud learning has driven point cloud completion into a new era. However, the information flows of most existing completion methods are solely feedforward, and high-level information is rarely reused to improve low-level feature learning. To this end, we propose a novel Feedback Network (FBNet) for point cloud completion,
Xuejun Yan, Hongyu Yan, Jingjing Wang 0005, Hang Du, Zhihong Wu, Di Xie, Shiliang Pu, Li Lu   +7 more
openaire   +3 more sources

Cyclic Global Guiding Network for Point Cloud Completion

Remote Sensing, 2022
The application of 3D scenes has gradually expanded in recent years. A 3D point cloud is unreliable when it is acquired because of the performance of the sensor. Therefore, it causes difficulties in utilization. Point cloud completion can reconstruct and
Ming Wei, Ming Zhu, Yaoyuan Zhang, Jiaqi Sun, Jiarong Wang   +4 more
doaj   +2 more sources

Multi-Branch and Multi-Scale Point Cloud Completion Network [PDF]

Jisuanji gongcheng
This paper proposes a point cloud completion network based on multi-branch multi-scale feature fusion because the existing networks cannot extract high-quality global and local features of point clouds simultaneously and lose point cloud detail and ...
CHEN Xiaolei, WANG Rong
doaj   +2 more sources