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PointâPC: Point cloud completion guided by prior knowledge via causal inference
CAAI Transactions on Intelligence TechnologyThe goal of point cloud completion is to reconstruct raw scanned point clouds acquired from incomplete observations due to occlusion and restricted viewpoints.
Xuesong Gao +4 more
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PoinTr: Diverse Point Cloud Completion with Geometry-Aware Transformers [PDF]
IEEE International Conference on Computer Vision, 2021 Point clouds captured in real-world applications are of-ten incomplete due to the limited sensor resolution, single viewpoint, and occlusion. Therefore, recovering the complete point clouds from partial ones becomes an indispensable task in many ...
Xumin Yu +5 more
semanticscholar +1 more source
SnowflakeNet: Point Cloud Completion by Snowflake Point Deconvolution with Skip-Transformer [PDF]
IEEE International Conference on Computer Vision, 2021 Point cloud completion aims to predict a complete shape in high accuracy from its partial observation. However, previous methods usually suffered from discrete nature of point cloud and unstructured prediction of points in local regions, which makes it ...
Peng Xiang +6 more
semanticscholar +1 more source
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
Zhang, Xuancheng +7 more
openaire +2 more sources
HSPC-Net: A hierarchical shape-preserving completion network for machine part point cloud completion [PDF]
PLoS ONEYuchao Jiang, Honghui Fan, Hongjin Zhu
doaj +2 more sources
SeedFormer: Patch Seeds based Point Cloud Completion with Upsample Transformer [PDF]
European Conference on Computer Vision, 2022 Point cloud completion has become increasingly popular among generation tasks of 3D point clouds, as it is a challenging yet indispensable problem to recover the complete shape of a 3D object from its partial observation.
Hao Zhou +6 more
semanticscholar +1 more source
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
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DAPointMamba: Domain Adaptive Point Mamba for Point Cloud Completion
Proceedings of the AAAI Conference on Artificial IntelligenceDomain adaptive point cloud completion (DA PCC) aims to narrow the geometric and semantic discrepancies between the labeled source and unlabeled target domains.
Yinghui Li +6 more
semanticscholar +3 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.
Spurek, Przemyslaw +7 more
openaire +3 more sources
PF-Net: Point Fractal Network for 3D Point Cloud Completion [PDF]
Computer Vision and Pattern Recognition, 2020 In this paper, we propose a Point Fractal Network (PF-Net), a novel learning-based approach for precise and high-fidelity point cloud completion. Unlike existing point cloud completion networks, which generate the overall shape of the point cloud from ...
Zitian Huang +4 more
semanticscholar +1 more source