Multi-Normal Estimation via Pair Consistency Voting

This paper presents a unified definition for point cloud normal of feature and non-feature points, which allows feature points to possess multiple normals. This definition facilitates several succeeding operations, such as feature points extraction and point cloud filtering.

We also develop a feature preserving normal estimation method which outputs multiple normals per feature point. In addition, we introduce an error measure compatible with traditional normal estimators, and present the first benchmark for normal estimation, composed of 152 synthesized data with various features and sampling densities, and 288 real scans with different noise levels.

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Recommended citation: Jie Zhang, Junjie Cao (co-first authors), Xiuping Liu, Anka He Chen, Bo Li, Ligang Liu. (2018). "Multi-Normal Estimation via Pair Consistency Voting." IEEE Transactions on Visualization and Computer Graphics.
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