Completing an unordered partial point cloud is a challenging task. Existing approaches that rely on decoding a latent feature to recover the complete shape, often lead to the completed point cloud being over-smoothing, losing details, and noisy. Instead of decoding a whole shape, we propose to decode and refine a low-resolution (low-res) point cloud first, and then perform a patch-wise noise-aware upsampling rather than interpolating the whole sparse point cloud at once, which tends to lose details. Regarding the possibility of lacking details of the initially decoded low-res point cloud, we propose an iterative refinement to recover the geometric details and a symmetrization process to preserve the trustworthy information from the input partial point cloud. After obtaining a sparse and complete point cloud, we propose a patch-wise upsampling strategy. Patch-based upsampling allows to recover fine details better rather than decoding a whole shape. The patch extraction approach is to generate training patch pairs between the sparse and ground-truth point clouds with an outlier removal step to suppress the noisy points from the sparse point cloud. Together with the low-res recovery, our whole pipeline can achieve high-fidelity point cloud completion. Comprehensive evaluations are provided to demonstrate the effectiveness of the proposed method and its components.
@article{point-cloud-completion,title={High-fidelity point cloud completion with low-resolution recovery and noise-aware upsampling},journal={Graphical Models},volume={126},pages={101173},year={2023},issn={1524-0703},doi={https://doi.org/10.1016/j.gmod.2023.101173},url={https://www.sciencedirect.com/science/article/pii/S1524070323000048},author={Li, Ren-Wu and Wang, Bo and Gao, Lin and Zhang, Ling-Xiao and Li, Chun-Peng},keywords={Point cloud completion, Point cloud analysis, Deep learning, Computer vision},}
E3Sym: Leveraging E(3) Invariance for Unsupervised 3D Planar Reflective Symmetry Detection
Ren-Wu Li, Ling-Xiao Zhang, Chun-Peng Li, and
2 more authors
In International Conference on Computer Vision (ICCV), 2023
