IMFIT: Normal Estimation via Learning Neural Implicit Surface

This paper aims to predict accurate unoriented normals from unorganized 3D point clouds, possibly containing noise and varying densities. Previous methods for unoriented normal estimation either fit an explicit surface from a local patch to infer normals or directly regress normals using neural networks. However, both approaches have limitations. Fitting explicit surfaces can be susceptible to challenging regions like sharp features due to the limited representation power of explicit surfaces. Meanwhile, directly regressing normals using neural networks lacks geometric interpretation. Different from previous works, we propose estimating normals by learning a neural implicit surface for a local patch. Our proposed neural implicit surface function has greater representation flexibility, increasing accuracy and robustness, even for complex shapes. Furthermore, we incorporate positional encoding with self-attention into our neural implicit surface function, refining the estimated normals further. Experimental results demonstrate that the proposed approach outperforms existing baselines on the PCPNet and SceneNN datasets, achieving state-of-the-art results.