MSECNet: Accurate and Robust Normal Estimation for 3D Point Clouds by Multi-Scale Edge Conditioning-Reference-Cited by-同舟云学术

MSECNet: Accurate and Robust Normal Estimation for 3D Point Clouds by Multi-Scale Edge Conditioning

Published:2023-10-26 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 31st ACM International Conference on Multimedia
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Author:

Xiu Haoyi¹^ORCID,Liu Xin¹^ORCID,Wang Weimin¹^ORCID,Kim Kyoung-Sook¹^ORCID,Matsuoka Masashi²^ORCID

Affiliation:

1. National Institute of Advanced Industrial Science and Technology (AIST), Tokyo, Japan

2. Tokyo Institute of Technology, Tokyo, Japan

Funder

JSPS Grant-in-Aid for Scientific Research

New Energy and Industrial Technology Development Organization (NEDO)

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3581783.3613762

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3. Pablo Arbelaez , Michael Maire , Charless Fowlkes , and Jitendra Malik . 2010. Contour detection and hierarchical image segmentation . IEEE transactions on pattern analysis and machine intelligence, Vol. 33 , 5 ( 2010 ), 898--916. Pablo Arbelaez, Michael Maire, Charless Fowlkes, and Jitendra Malik. 2010. Contour detection and hierarchical image segmentation. IEEE transactions on pattern analysis and machine intelligence, Vol. 33, 5 (2010), 898--916.

4. DeepFit: 3D Surface Fitting via Neural Network Weighted Least Squares

5. Nesti-Net: Normal Estimation for Unstructured 3D Point Clouds Using Convolutional Neural Networks