ImMesh: An Immediate LiDAR Localization and Meshing Framework
Not on the map. Why?
Abstract (arXiv)
In this paper, we propose a novel LiDAR(-inertial) odometry and mapping framework to achieve the goal of simultaneous localization and meshing in real-time. This proposed framework termed ImMesh comprises four tightly-coupled modules: receiver, localization, meshing, and broadcaster. The localization module utilizes the prepossessed sensor data from the receiver, estimates the sensor pose online by registering LiDAR scans to maps, and dynamically grows the map. Then, our meshing module takes the registered LiDAR scan for incrementally reconstructing the triangle mesh on the fly. Finally, the real-time odometry, map, and mesh are published via our broadcaster. The key contribution of this work is the meshing module, which represents a scene by an efficient hierarchical voxels structure, performs fast finding of voxels observed by new scans, and reconstructs triangle facets in each voxel in an incremental manner. This voxel-wise meshing operation is delicately designed for the purpose of efficiency; it first performs a dimension reduction by projecting 3D points to a 2D local plane contained in the voxel, and then executes the meshing operation with pull, commit and push steps for incremental reconstruction of triangle facets. To the best of our knowledge, this is the first work in literature that can reconstruct online the triangle mesh of large-scale scenes, just relying on a standard CPU without GPU acceleration. To share our findings and make contributions to the community, we make our code publicly available on our GitHub: https://github.com/hku-mars/ImMesh.
Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). Builds on 26 of its 107 references that are in the map. First posted on arXiv 12 Jan 2023.
Builds on
26 of 107 references.
References · 26
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- 2020TartanAir: A Dataset to Push the Limits of Visual SLAMcited by 494
- 2021NeuralRecon: Real-Time Coherent 3D Reconstruction from Monocular Videocited by 241
- 2020Atlas: End-to-End 3D Scene Reconstruction from Posed Imagescited by 206
- 2019Kimera: an Open-Source Library for Real-Time Metric-Semantic Localization and Mappingcited by 181
- 2021FAST-LIO2: Fast Direct LiDAR-inertial Odometrycited by 170
- 2016Voxblox: Incremental 3D Euclidean Signed Distance Fields for On-Board MAV Planningcited by 148
- 2015On-Manifold Preintegration for Real-Time Visual-Inertial Odometrycited by 95
- 2020Flightmare: A Flexible Quadrotor Simulatorcited by 69
- 2022MeshLoc: Mesh-Based Visual Localizationcited by 62
- 2022A Survey on Metaverse: Fundamentals, Security, and Privacycited by 51
- 2021R3LIVE: A Robust, Real-time, RGB-colored, LiDAR-Inertial-Visual tightly-coupled state Estimation and mapping packagecited by 49
14 more
- 2020FUEL: Fast UAV Exploration using Incremental Frontier Structure and Hierarchical Planningcited by 42
- 2019SurfelWarp: Efficient Non-Volumetric Single View Dynamic Reconstructioncited by 31
- 2018SurfelMeshing: Online Surfel-Based Mesh Reconstructioncited by 29
- 2022NTU VIRAL: A Visual-Inertial-Ranging-Lidar Dataset, From an Aerial Vehicle Viewpointcited by 28
- 2019Loam_livox: A fast, robust, high-precision LiDAR odometry and mapping package for LiDARs of small FoVcited by 27
- 2021MULLS: Versatile LiDAR SLAM via Multi-metric Linear Least Squarecited by 27
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- 2020RAPTOR: Robust and Perception-aware Trajectory Replanning for Quadrotor Fast Flightcited by 20
- 2021Efficient and Probabilistic Adaptive Voxel Mapping for Accurate Online LiDAR Odometrycited by 16
- 2021R2LIVE: A Robust, Real-time, LiDAR-Inertial-Visual tightly-coupled state Estimator and mappingcited by 12
- 2019A fast, complete, point cloud based loop closure for LiDAR odometry and mappingcited by 9
- 2021ikd-Tree: An Incremental K-D Tree for Robotic Applicationscited by 9
- 2022R3LIVE++: A Robust, Real-time, Radiance reconstruction package with a tightly-coupled LiDAR-Inertial-Visual state Estimatorcited by 3
- 2022MARSIM: A light-weight point-realistic simulator for LiDAR-based UAVscited by 3
Built on by
Citing papers per year: 2023: 1; 2024: 5; 2025: 5.
11 papers.
- 2024The Oxford Spires Dataset: Benchmarking Large-Scale LiDAR-Visual Localisation, Reconstruction and Radiance Field Methodscited by 27
- 2023DGNR: Density-Guided Neural Point Rendering of Large Driving Scenescited by 9
- 2025GS-LIVO: Real-Time LiDAR, Inertial, and Visual Multi-sensor Fused Odometry with Gaussian Mappingcited by 8
- 2024Visual Localization in 3D Maps: Comparing Point Cloud, Mesh, and NeRF Representationscited by 4
- 2024Neural Surface Reconstruction and Rendering for LiDAR-Visual Systemscited by 3
- 2024Real-Time Metric-Semantic Mapping for Autonomous Navigation in Outdoor Environmentscited by 2
- 2024QuadricsReg: Large-Scale Point Cloud Registration using Quadric Primitivescited by 1
- 2025KN-LIO: Geometric Kinematics and Neural Field Coupled LiDAR-Inertial Odometrycited by 0
- 2025CAR-LOAM: Color-Assisted Robust LiDAR Odometry and Mappingcited by 0
- 2025HessianForge: Scalable LiDAR reconstruction with Physics-Informed Neural Representation and Smoothness Energy Constraintscited by 0
- 2025Mesh-Learner: Texturing Mesh with Spherical Harmonicscited by 0
- 2025Mesh-Learner: Texturing Mesh with Spherical Harmonics28 Apr 2025
- 2025HessianForge: Scalable LiDAR reconstruction with Physics-Informed Neural Representation and Smoothness Energy Constraints11 Mar 2025
- 2025CAR-LOAM: Color-Assisted Robust LiDAR Odometry and Mapping24 Feb 2025
- 2025GS-LIVO: Real-Time LiDAR, Inertial, and Visual Multi-sensor Fused Odometry with Gaussian Mapping15 Jan 2025
- 2025KN-LIO: Geometric Kinematics and Neural Field Coupled LiDAR-Inertial Odometry8 Jan 2025
- 2024QuadricsReg: Large-Scale Point Cloud Registration using Quadric Primitives4 Dec 2024
- 2024Real-Time Metric-Semantic Mapping for Autonomous Navigation in Outdoor Environments30 Nov 2024
- 2024The Oxford Spires Dataset: Benchmarking Large-Scale LiDAR-Visual Localisation, Reconstruction and Radiance Field Methods15 Nov 2024
- 2024Neural Surface Reconstruction and Rendering for LiDAR-Visual Systems9 Sep 2024
- 2024Visual Localization in 3D Maps: Comparing Point Cloud, Mesh, and NeRF Representations21 Aug 2024
- 2023DGNR: Density-Guided Neural Point Rendering of Large Driving Scenes28 Nov 2023