Robust point cloud normal estimation via multi-level critical point aggregation

We propose a multi-level critical point aggregation architecture based on a graph attention mechanism for 3D point cloud normal estimation, which can efficiently focus on locally important points during the feature extraction process. Wherein, the local feature aggregation (LFA) module and the global feature refinement (GFR) module are designed to accurately identify critical points which are geometrically closer to tangent plane for surface fitting at both local and global levels. Specifically, the LFA module captures significant local information from neighboring points with strong geometric correlations to the query point in the low-level feature space. The GFR module enhances the exploration of global geometric correlations in the high-level feature space, allowing the network to focus precisely on critical global points. To address indistinguishable features in the low-level space, we implement a stacked LFA structure. This structure transfers essential adjacent information across multiple levels, enabling deep feature aggregation layer by layer. Then the GFR module can leverage robust local geometric information and refines it into comprehensive global features. Our multi-level point-aware architecture improves the stability and accuracy of surface fitting and normal estimation, even in the presence of sharp features, high noise or anisotropic structures. Experimental results demonstrate that our method is competitive and achieves stable performance on both synthetic and real-world datasets. Code is available at https://github.com/CharlesLee96/NormalEstimation.