1 research outputs found
MSECNet: Accurate and Robust Normal Estimation for 3D Point Clouds by Multi-Scale Edge Conditioning
Estimating surface normals from 3D point clouds is critical for various
applications, including surface reconstruction and rendering. While existing
methods for normal estimation perform well in regions where normals change
slowly, they tend to fail where normals vary rapidly. To address this issue, we
propose a novel approach called MSECNet, which improves estimation in normal
varying regions by treating normal variation modeling as an edge detection
problem. MSECNet consists of a backbone network and a multi-scale edge
conditioning (MSEC) stream. The MSEC stream achieves robust edge detection
through multi-scale feature fusion and adaptive edge detection. The detected
edges are then combined with the output of the backbone network using the edge
conditioning module to produce edge-aware representations. Extensive
experiments show that MSECNet outperforms existing methods on both synthetic
(PCPNet) and real-world (SceneNN) datasets while running significantly faster.
We also conduct various analyses to investigate the contribution of each
component in the MSEC stream. Finally, we demonstrate the effectiveness of our
approach in surface reconstruction.Comment: Accepted for ACM MM 202