Name
Abstract | ||
|---|---|---|
The captured 3D point clouds by depth cameras and 3D scanners are often corrupted by noise, so point cloud denoising is typically required for downstream applications. We observe that: (i) the scale of the local neighborhood has a significant effect on the denoising performance against different noise levels, point intensities, as well as various kinds of local details; (ii) non-iteratively evolving a noisy input to its noise-free version is non-trivial; (iii) both traditional geometric methods and learning-based methods often lose geometric features with denoising iterations, and (iv) most objects can be regarded as piece-wise smooth surfaces with a small number of features. Motivated by these observations, we propose a novel and task-specific point cloud denoising network, named RePCD-Net, which consists of four key modules: (i) a recurrent network architecture to effectively remove noise; (ii) an RNN-based multi-scale feature aggregation module to extract adaptive features in different denoising stage; (iii) a recurrent propagation layer to enhance the geometric feature perception across stages; and (iv) a feature-aware CD loss to regularize the predictions towards multi-scale geometric details. Extensive qualitative and quantitative evaluations demonstrate the effectiveness and superiority of our method over state-of-the-arts, in terms of noise removal and feature preservation. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1007/s11263-021-01564-7 | INTERNATIONAL JOURNAL OF COMPUTER VISION |
Keywords | DocType | Volume |
Point cloud, 3D deep learning, RNN, Multi-scale feature learning, Geometric feature preservation | Journal | 130 |
Issue | ISSN | Citations |
3 | 0920-5691 | 0 |
PageRank | References | Authors |
0.34 | 0 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Honghua Chen | 1 | 6 | 2.76 |
| Zeyong Wei | 2 | 0 | 0.68 |
| Xianzhi Li | 3 | 0 | 0.34 |
| Yabin Xu | 4 | 0 | 1.69 |
| Mingqiang Wei | 5 | 125 | 22.66 |
| Jun Wang | 6 | 0 | 1.01 |