Nesti-Net: Normal Estimation for Unstructured 3D Point Clouds using Convolutional Neural Networks

被引:79
作者
Ben-Shabat, Yizhak [1 ]
Lindenbaum, Michael [2 ]
Fischer, Anath [1 ]
机构
[1] Techion IIT, Mech Engn, Haifa, Israel
[2] Techion IIT, Comp Sci, Haifa, Israel
来源
2019 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR 2019) | 2019年
关键词
ROBUST NORMAL ESTIMATION; SURFACE RECONSTRUCTION; CLASSIFICATION;
D O I
10.1109/CVPR.2019.01035
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
In this paper, we propose a normal estimation method for unstructured 3D point clouds. This method, called Nesti-Net, builds on a new local point cloud representation which consists of multi-scale point statistics (MuPS), estimated on a local coarse Gaussian grid. This representation is a suitable input to a CNN architecture. The normals are estimated using a mixture of-experts (MoE) architecture, which relies on a data driven approach for selecting the optimal scale around each point and encourages sub -network specialization. Interesting insights into the network's resource distribution are provided. The scale prediction significantly improves robustness to different noise levels, point density variations and different levels of detail. We achieve state-of-the-art results on a benchmark synthetic dataset and present qualitative results on real scanned scenes.
引用
收藏
页码:10104 / 10112
页数:9
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