Ray-Path-Aware Virtual Point Removal on 2D Layer-Wise Nearest Point Map

When LiDAR scans a scene with reflective materials, it of-ten produces erroneous virtual points, which resemble real structuresand degrade downstream 3D perception. Conventional methods rely onmulti-stage pipelines that combine 3D surface reconstruction with heuris-tic symmetry detection, making them highly sensitive to surface noise,reflection irregularities, and accumulated estimation errors across stages.To address this, we propose an end-to-end framework that reformu-lates virtual point removal as ray-aligned geometric reasoning in the 2Dequirectangular projection domain, eliminating the dependency on ex-plicit surface modeling. Specifically, we introduce a Layer-Wise NearestPoint Map, which organizes multi-return LiDAR points by layer corre-spondence rather than fixed distance intervals, preserving geometric con-tinuity between real and reflected surfaces. Building on this representa-tion, we propose a Ray-Path-Aware Attention mechanism that constrainsfeature aggregation along physically plausible reflection trajectories de-rived from surface normals and incident ray directions. This physicallyguided attention enables the network to learn reflection-induced symme-try between real and reflected regions without stage-wise surface estima-tion. Extensive experiments on real-world LiDAR datasets demonstratethat our method achieves more accurate and stable virtual point removalthan prior symmetry-based approaches.