A Region-Growing Supervised Geometry-Weighted Transformer for Normal Estimation

This paper addresses the challenge of accurately estimating unoriented normals for 3D point clouds, particularly in the presence of noise, varying densities, and sharp features. To overcome these challenges, we introduce a novel network architecture that integrates a geometry-weighted transformer module with a dual backbone network. This combined architecture enables accurate prediction of normals from patch embeddings that encode edge and spatial features. Additionally, we propose a region-growing loss to supervise the network, which can aid in normal estimation for sharp features by segmenting input patches based on normal discontinuities, thus avoiding the over-smoothness for normals in the sharp feature areas. Extensive experiments on PCPNet and SceneNN datasets highlight the effectiveness of our approach, leading to state-of-the-art performance against baseline methods.