Results 31 to 40 of about 381,180 (306)
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 +6 more
doaj +1 more source
Unsupervised Point Cloud Pre-training via Occlusion Completion [PDF]
2021 IEEE/CVF International Conference on Computer Vision (ICCV), 2021 We describe a simple pre-training approach for point clouds. It works in three steps: 1. Mask all points occluded in a camera view; 2. Learn an encoder-decoder model to reconstruct the occluded points; 3. Use the encoder weights as initialisation for downstream point cloud tasks.
Wang, Hanchen +4 more
openaire +3 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 +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
Hyperspherical Embedding for Point Cloud Completion
2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023 Most real-world 3D measurements from depth sensors are incomplete, and to address this issue the point cloud completion task aims to predict the complete shapes of objects from partial observations. Previous works often adapt an encoder-decoder architecture, where the encoder is trained to extract embeddings that are used as inputs to generate ...
Zhang, Junming +3 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
FBNet: Feedback Network for Point Cloud Completion
, 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 +7 more
openaire +2 more sources
SPCNet: Stepwise Point Cloud Completion Network
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 +7 more
openaire +2 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 +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