Results 31 to 40 of about 5,548,528 (274)
Cascaded Refinement Network for Point Cloud Completion [PDF]
2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020 CVPR2020
Xiaogang Wang 0008 +2 more
openaire +3 more sources
2023 IEEE International Conference on Robotics and Automation (ICRA), 2023 In the practical application of point cloud completion tasks, real data quality is usually much worse than the CAD datasets used for training. A small amount of noisy data will usually significantly impact the overall system's accuracy. In this paper, we propose a quality evaluation network to score the point clouds and help judge the quality of the ...
Jieqi Shi +3 more
openaire +4 more sources
PointCA: Evaluating the Robustness of 3D Point Cloud Completion Models against Adversarial Examples
, 2023 Point cloud completion, as the upstream procedure of 3D recognition and segmentation, has become an essential part of many tasks such as navigation and scene understanding.
Li, M +15 more
core +1 more source
Learning Local Displacements for Point Cloud Completion
2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022 We propose a novel approach aimed at object and semantic scene completion from a partial scan represented as a 3D point cloud. Our architecture relies on three novel layers that are used successively within an encoder-decoder structure and specifically developed for the task at hand.
Yida Wang 0001 +3 more
openaire +3 more sources
SCPNet: Semantic Scene Completion on Point Cloud
2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023 CVPR ...
Zhaoyang Xia +7 more
openaire +4 more sources
Point Cloud Completion by Learning Shape Priors [PDF]
2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2020 In view of the difficulty in reconstructing object details in point cloud completion, we propose a shape prior learning method for object completion. The shape priors include geometric information in both complete and the partial point clouds. We design a feature alignment strategy to learn the shape prior from complete points, and a coarse to fine ...
Xiaogang Wang 0008 +2 more
openaire +2 more sources
DF-Net: Dynamic and Folding Network for 3D Point Cloud Completion
IEEE Access, 2022 The development of 3D sensors encourages researchers to process point cloud data directly. Point cloud data requires less memory but conveys more detailed 3D shape information.
Yao Xiao +4 more
doaj +1 more source
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 +4 more
doaj +1 more source
Sparse-to-Dense Multi-Encoder Shape Completion of Unstructured Point Cloud
IEEE Access, 2020 Unstructured point clouds are a representative shape representation of real-world scenes in 3D vision and graphics. Incompletion inevitably arises, due to the way the set of unorganized points is captured, e.g., as fusion of depth images, merged laser ...
Yanjun Peng +6 more
doaj +1 more source
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 +4 more
doaj +1 more source