Incremental Semantics-Aided Meshing from LiDAR-Inertial Odometry and RGB Direct Label Transfer

• URL: http://arxiv.org/abs/2604.09478v1
• Date: Fri, 10 Apr 2026 16:41:47 GMT
• Title: Incremental Semantics-Aided Meshing from LiDAR-Inertial Odometry and RGB Direct Label Transfer
• Abstract summary: We propose a modular, incremental RGB+LiDAR pipeline that generates semantics-aided high-quality meshes from indoor scans.<n>A vision foundation model labels each incoming RGB frame; labels are incrementally projected and fused onto a LiDAR-inertial odometry map.<n>The resulting semantically labelled meshes are of value when reconstructing Universal Scene Description (USD) assets.
• Score: 3.2208886039079925
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Geometric high-fidelity mesh reconstruction from LiDAR-inertial scans remains challenging in large, complex indoor environments -- such as cultural buildings -- where point cloud sparsity, geometric drift, and fixed fusion parameters produce holes, over-smoothing, and spurious surfaces at structural boundaries. We propose a modular, incremental RGB+LiDAR pipeline that generates incremental semantics-aided high-quality meshes from indoor scans through scan frame-based direct label transfer. A vision foundation model labels each incoming RGB frame; labels are incrementally projected and fused onto a LiDAR-inertial odometry map; and an incremental semantics-aware Truncated Signed Distance Function (TSDF) fusion step produces the final mesh via marching cubes. This frame-level fusion strategy preserves the geometric fidelity of LiDAR while leveraging rich visual semantics to resolve geometric ambiguities at reconstruction boundaries caused by LiDAR point-cloud sparsity and geometric drift. We demonstrate that semantic guidance improves geometric reconstruction quality; quantitative evaluation is therefore performed using geometric metrics on the Oxford Spires dataset, while results from the NTU VIRAL dataset are analyzed qualitatively. The proposed method outperforms state-of-the-art geometric baselines ImMesh and Voxblox, demonstrating the benefit of semantics-aided fusion for geometric mesh quality. The resulting semantically labelled meshes are of value when reconstructing Universal Scene Description (USD) assets, offering a path from indoor LiDAR scanning to XR and digital modeling.

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