MODNet: Multi-offset Point Cloud Denoising Network Customized for
Multi-scale Patches
- URL: http://arxiv.org/abs/2208.14160v2
- Date: Thu, 1 Sep 2022 07:31:19 GMT
- Title: MODNet: Multi-offset Point Cloud Denoising Network Customized for
Multi-scale Patches
- Authors: Anyi Huang, Qian Xie, Zhoutao Wang, Dening Lu, Mingqiang Wei, Jun Wang
- Abstract summary: We propose a Multi-offset Denoising Network (MODNet) customized for multi-scale patches.
A multi-scale perception module is designed to embed multi-scale geometric information for each scale feature.
Experiments demonstrate that our method achieves new state-of-the-art performance on both synthetic and real-scanned datasets.
- Score: 14.078359217301973
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The intricacy of 3D surfaces often results cutting-edge point cloud denoising
(PCD) models in surface degradation including remnant noise, wrongly-removed
geometric details. Although using multi-scale patches to encode the geometry of
a point has become the common wisdom in PCD, we find that simple aggregation of
extracted multi-scale features can not adaptively utilize the appropriate scale
information according to the geometric information around noisy points. It
leads to surface degradation, especially for points close to edges and points
on complex curved surfaces. We raise an intriguing question -- if employing
multi-scale geometric perception information to guide the network to utilize
multi-scale information, can eliminate the severe surface degradation problem?
To answer it, we propose a Multi-offset Denoising Network (MODNet) customized
for multi-scale patches. First, we extract the low-level feature of three
scales patches by patch feature encoders. Second, a multi-scale perception
module is designed to embed multi-scale geometric information for each scale
feature and regress multi-scale weights to guide a multi-offset denoising
displacement. Third, a multi-offset decoder regresses three scale offsets,
which are guided by the multi-scale weights to predict the final displacement
by weighting them adaptively. Experiments demonstrate that our method achieves
new state-of-the-art performance on both synthetic and real-scanned datasets.
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