Geometry and Learning Co-Supported Normal Estimation for Unstructured Point Cloud

作者：Haoran Zhou, Honghua Chen, Yidan Feng, Qiong Wang, Jing Qin, Haoran Xie, Fu Lee Wang, Mingqiang Wei, Jun Wang · 年份：2020 · DOI：10.1109/cvpr42600.2020.01325 · 被引用次数：42 · 研究领域：3D Shape Modeling and Analysis、3D Surveying and Cultural Heritage、Computer Graphics and Visualization Techniques

In this paper, we propose a normal estimation method for unstructured point cloud. We observe that geometric estimators commonly focus more on feature preservation but are hard to tune parameters and sensitive to noise, while learning-based approaches pursue an overall normal estimation accuracy but cannot well handle challenging regions such as surface edges. This paper presents a novel normal estimation method, under the co-support of geometric estimator and deep learning. To lowering the learning difficulty, we first propose to compute a suboptimal initial normal at each point by searching for a best fitting patch. Based on the computed normal field, we design a normal-based height map network (NH-Net) to fine-tune the suboptimal normals. Qualitative and quantitative evaluations demonstrate the clear improvements of our results over both traditional methods and learning-based methods, in terms of estimation accuracy and feature recovery.