Normal Estimation for 3D Point Clouds via Local Plane Constraint and Multi-scale Selection

• Published in: Computer aided design Vol. 129; p. 102916
• Main Authors: Zhou, Jun, Huang, Hua, Liu, Bin, Liu, Xiuping
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier Ltd 01.12.2020; Elsevier BV
• Subjects: Computer architecture; Cost analysis; Feature extraction; LPFC; Multi-scale selection; Multiscale analysis; Noise; Noise levels; Normal estimation; Optimization; Plane-aware features; Point cloud processing; Regression analysis; Robust to noise; Robustness (mathematics); Three dimensional models; Multi-scale selection; LPFC; Robust to noise; Normal estimation; Plane-aware features; Point cloud processing
• ISSN: 0010-4485; 1879-2685
• DOI: 10.1016/j.cad.2020.102916

In this paper, we propose a normal estimation method for unstructured 3D point clouds. In this method, a feature constraint mechanism called Local Plane Features Constraint (LPFC) is used and then a multi-scale selection strategy is introduced. The LPFC can be used in a single-scale point network architecture for a more stable normal estimation of the unstructured 3D point clouds. In particular, it can partly overcome the influence of noise on a large sampling scale compared to the other methods which only use regression loss for normal estimation. For more details, a subnetwork is built after point-wise features extracted layers of the network and it gives more constraints to each point of the local patch via a binary classifier in the end. Then we use multi-task optimization to train the normal estimation and local plane classification tasks simultaneously. Via LPFC, the normal estimation network could obtain more distinguish point-wise plane-aware features that can describe the differences of each point on the local patch. Finally, thanks to the distinguish features constraint, we can obtain a more robust and meaningful global feature that can be used to regress the normal of the local patch. Also, to integrate the advantages of multi-scale results, a scale selection strategy is adopted, which is a data-driven approach for selecting the optimal scale around each point and encourages subnetwork specialization. Specifically, we employed a subnetwork called Scale Estimation Network to extract scale weight information from multi-scale features. The multi-scale method can well reduce the cost time while persevere the estimation accuracy. More analysis is given about the relations between noise levels, local boundary, and scales in the experiment. These relationships can be a better guide to choosing particular scales for a particular model. Besides, the experimental result shows that our network can distinguish the points on the fitting plane accurately and this can be used to guide the normal estimation and our multi-scale method can improve the results well. Compared to some state-of-the-art surface normal estimators, our method is robust to noise and can achieve competitive results. •For single-scale normal estimation, Local Plane Features Constraint (LPFC) is used in our networks to ensure the robustness of the normal estimation network. Besides, the binary classifier used in our LPFC can well obtain the main part of the patch, especially when the sampling scale is large.•A scale selection strategy for scale prediction is employed in our method. In this paper, we propose a novel scale estimation network, which is used to select the most suitable scale of each point through a joint analysis of multiscale features extracted from single-scale networks.•The experiment shows outperformance results in single-scale and multi-scale compared some state-of-the-art surface normal estimators.