PlanarMesh: Building Compact 3D Meshes from LiDAR using Incremental Adaptive Resolution Reconstruction

Source: https://dynamic.robots.ox.ac.uk/projects/planarmesh/ Parent: https://dynamic.robots.ox.ac.uk/projects/

Jiahao Wang, Nived Chebrolu, Yifu Tao, Lintong Zhang, Ayoung Kim, Maurice Fallon

Accepted at the 2025 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2025)

Abstract: Building an online 3D LiDAR mapping system that produces a detailed surface reconstruction while remaining computationally efficient is a challenging task. In this paper, we present PlanarMesh, a novel incremental, mesh-based LiDAR reconstruction system that adaptively adjusts mesh resolution to achieve compact, detailed reconstructions in real-time. It introduces a new representation, planar-mesh, which combines plane modeling and meshing to capture both large surfaces and detailed geometry. The planar-mesh can be incrementally updated considering both local surface curvature and free-space information from sensor measurements. We employ a multi-threaded architecture with a Bounding Volume Hierarchy (BVH) for efficient data storage and fast search operations, enabling real-time performance. Experimental results show that our method achieves reconstruction accuracy on par with, or exceeding, state-of-the-art techniques—including truncated signed distance functions, occupancy mapping, and voxel-based meshing—while producing smaller output file sizes (10 times smaller than raw input and more than 5 times smaller than mesh-based methods) and maintaining real-time performance (around 2 Hz for a 64-beam sensor).

Arxiv

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Citation

@inproceedings{wang2025planarmesh,
  title={PlanarMesh: Building Compact 3D Meshes from LiDAR using Incremental Adaptive Resolution Reconstruction},
  author={Wang, Jiahao and Chebrolu, Nived and Tao, Yifu and Zhang, Lintong and Kim, Ayoung and Fallon, Maurice},
  booktitle={IEEE/RSJ Intl. Conf. on Intelligent Robots and Systems (IROS)}, 
  year={2025},
}