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.

翻译：从三维点云中估计表面法线对于表面重建和渲染等多种应用至关重要。现有法线估计方法在法线缓慢变化区域表现良好，但在法线快速变化区域往往失效。为解决这一问题，我们提出一种名为MSECNet的新方法，通过将法线变化建模视为边缘检测问题，提高了法线变化区域的估计精度。MSECNet由一个骨干网络和一个多尺度边缘条件化（MSEC）流组成。MSEC流通过多尺度特征融合和自适应边缘检测实现鲁棒边缘检测。随后，利用边缘条件化模块将检测到的边缘与骨干网络的输出结合，生成边缘感知表征。大量实验表明，MSECNet在合成数据集（PCPNet）和真实数据集（SceneNN）上的性能均优于现有方法，且运行速度显著更快。我们还进行了多项分析以研究MSEC流中每个组件的贡献。最后，我们展示了该方法在表面重建中的有效性。