Abstract
Point normals are fundamental geometric attributes of point clouds. To tackle traditional and learning-based technology problems, a normal estimation method is proposed based on geometric prior and deep-learning techniques. First, a multi-scale patch selection module is utilized to preserve surfaces features and details and to lower the learning difficulty of the subsequent network. Afterwards, combining local features and geometric prior, a normal optimization network is proposed to output the refined normals. Finally, when the average angle error metric is used, quantitative and qualitative experiments on synthetic and real- scanned data demonstrate that, compared with the mainstream point cloud normal estimation methods, the proposed method is more effective on both preserving features and robust to noise.
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