most citedRefine-Net: Normal Refinement Neural Network for Noisy Point Clouds

4 citations · 8 across the 5 of their papers we have counts for

collaborators

5 papers

cs.CV20221 cited

LBF:Learnable Bilateral Filter For Point Cloud Denoising

Huajian Si, Zeyong Wei, Zhe Zhu +4

Bilateral filter (BF) is a fast, lightweight and effective tool for image denoising and well extended to point cloud denoising. However, it often involves continual yet manual para…

cs.CV20221 cited

GeoGCN: Geometric Dual-domain Graph Convolution Network for Point Cloud Denoising

Zhaowei Chen, Peng Li, Zeyong Wei +4

We propose GeoGCN, a novel geometric dual-domain graph convolution network for point cloud denoising (PCD). Beyond the traditional wisdom of PCD, to fully exploit the geometric inf…

cs.CV20221 cited

SPCNet: Stepwise Point Cloud Completion Network

Fei Hu, Honghua Chen, Xuequan Lu +5

How will you repair a physical object with large missings? You may first recover its global yet coarse shape and stepwise increase its local details. We are motivated to imitate th…

cs.CV20221 cited

Deep Algebraic Fitting for Multiple Circle Primitives Extraction from Raw Point Clouds

Zeyong Wei, Honghua Chen, Hao Tang +3

The shape of circle is one of fundamental geometric primitives of man-made engineering objects. Thus, extraction of circles from scanned point clouds is a quite important task in 3…

cs.CV20224 cited

Refine-Net: Normal Refinement Neural Network for Noisy Point Clouds

Haoran Zhou, Honghua Chen, Yingkui Zhang +6

Point normal, as an intrinsic geometric property of 3D objects, not only serves conventional geometric tasks such as surface consolidation and reconstruction, but also facilitates…