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

arXiv: https://arxiv.org/abs/2604.09478 · HTML: https://mlsanity.com/paper/2604.09478/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 15 references that are in the map.
- First posted on arXiv 10 Apr 2026.

## Builds on (most cited first, 9 of 9)

- [Kimera: an Open-Source Library for Real-Time Metric-Semantic Localization and Mapping](https://mlsanity.com/paper/1910.02490/) (2019)
- [FAST-LIO2: Fast Direct LiDAR-inertial Odometry](https://mlsanity.com/paper/2107.06829/) (2021)
- [Voxblox: Incremental 3D Euclidean Signed Distance Fields for On-Board MAV Planning](https://mlsanity.com/paper/1611.03631/) (2016)
- [MaskFusion: Real-Time Recognition, Tracking and Reconstruction of Multiple Moving Objects](https://mlsanity.com/paper/1804.09194/) (2018)
- [Volumetric Instance-Aware Semantic Mapping and 3D Object Discovery](https://mlsanity.com/paper/1903.00268/) (2019)
- [Foundations of Spatial Perception for Robotics: Hierarchical Representations and Real-time Systems](https://mlsanity.com/paper/2305.07154/) (2023)
- [Voxgraph: Globally Consistent, Volumetric Mapping using Signed Distance Function Submaps](https://mlsanity.com/paper/2004.13154/) (2020)
- [Articulate3D: Holistic Understanding of 3D Scenes as Universal Scene Description](https://mlsanity.com/paper/2412.01398/) (2024)
- [Mesh-LOAM: Real-time Mesh-Based LiDAR Odometry and Mapping](https://mlsanity.com/paper/2312.15630/) (2023)

## Abstract (arXiv)

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.