ESNE: Efficient Surface Normal Estimation for LiDAR Point Clouds with Sequential Modeling and Variability Guidance

While surface normal estimation from point clouds is a fun-damental problem in computer vision, there has been limited explorationon LiDAR point clouds. Recent approaches adopt Transformer-basedmodels designed for large-scale point clouds to achieve single-step esti-mation, still struggling to estimate surface normals with high computa-tional efficiency and accuracy. In this paper, we propose an efficient sur-face normal estimation method for LiDAR point clouds, termed ESNE.It converts point clouds into a sequence data format and leverages astate space model to capture long-range dependencies. To capture lo-cal structure, the proposed method introduces a local attention moduleincorporating an efficient and explicit search for neighboring points onthe sequence. Furthermore, the proposed method introduces a featurerefinement module using structural variability as guidance to enhancethe shape representations of local regions. Experimental results on large-scale LiDAR point cloud datasets demonstrate that the proposed methodimproves the mean accuracy by over 33.4% and achieves more than twicethe inference speed compared to state-of-the-art methods.