Latent Tangent Space Representation for Normal Estimation

Point cloud processing is rapidly expanding the applicable scenarios in the industry. The surface normal is a fundamental feature for various point cloud processing tasks. Recently, deep supervised normal estimators outperform traditional normal estimation methods by adapting to dataset statistics. However, existing normal estimation methods mainly adopt hand-crafted features or complicated networks borrowed from other tasks and make less effort to design network models specifically for the problem. Instead of regressing the normal vector directly, we propose a simple deep network to estimate the normal vector based on a latent tangent space representation learned in the network. We call the network tangent represent learning network (TRNet). For each query point, the tangent space representation is a set of latent points spanning the tangent plane of it. The representation is generated using only the coordinates of its neighbors and regularized by a differentiable random sample consensus like component, which makes TRNet more compact and effective for normal estimation. We also design a compact multiscale network, denoted by multiresolution tangent representation learning network (MTRNet), to boost estimations from multiple TRNet trained with different fixed neighborhood size. Our TRNet and MTRNet perform favorably against state-of-the-art methods on synthesized data and real scenarios with far smaller model size.