Results 21 to 30 of about 5,366,984 (266)
Attention-Enhanced Feature-Based Point Cloud Completion Network for Precision Parts [PDF]
SensorsWhen acquiring point cloud data of precision parts using 3D scanning devices, occlusion or equipment limitations often lead to sparse and incomplete data, resulting in the distortion or loss of key geometric features.
Hongfei Zu +5 more
doaj +2 more sources
Generative adversarial networks for high-fidelity 3D point cloud completion [PDF]
Scientific Reports3D 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 +3 more
doaj +2 more sources
View-Guided Point Cloud Completion [PDF]
2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021 This paper presents a view-guided solution for the task of point cloud completion. Unlike most existing methods directly inferring the missing points using shape priors, we address this task by introducing ViPC (view-guided point cloud completion) that takes the missing crucial global structure information from an extra single-view image. By leveraging
Xuancheng Zhang +7 more
openaire +3 more sources
Temporal Point Cloud Completion With Pose Disturbance [PDF]
IEEE Robotics and Automation Letters, 2022 Point clouds collected by real-world sensors are always unaligned and sparse, which makes it hard to reconstruct the complete shape of object from a single frame of data. In this work, we manage to provide complete point clouds from sparse input with pose disturbance by limited translation and rotation.
Jieqi Shi +4 more
openaire +5 more sources
HyperPocket: Generative Point Cloud Completion
2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2022 Scanning real-life scenes with modern registration devices typically give incomplete point cloud representations, mostly due to the limitations of the scanning process and 3D occlusions. Therefore, completing such partial representations remains a fundamental challenge of many computer vision applications.
Przemyslaw Spurek +7 more
openaire +3 more sources
Enhancing Performance of Point Cloud Completion Networks with Consistency Loss [PDF]
Point cloud completion networks are conventionally trained to minimize the disparities between the completed point cloud and the ground-truth counterpart.
Kong, Seung-Hyun +2 more
core +9 more sources
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 ...
Junming Zhang +3 more
openaire +3 more sources
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