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.

MSECNet 是一种改进正常变化区域估计的方法，通过将正常变化建模视为边缘检测问题，采用多尺度特征融合和自适应边缘检测实现强大的边缘检测，将检测到的边缘与主干网络的输出结合，以产生边缘感知表示。在合成和现实世界数据集上，MSECNet 在性能上优于现有方法，且运行速度显著更快，表明该方法在表面重建中的有效性。