HVOFusion: Incremental Mesh Reconstruction Using Hybrid Voxel Octree

HVOFusion: Incremental Mesh Reconstruction Using Hybrid Voxel Octree

Shaofan Liu, Junbo Chen, Jianke Zhu

Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence
Main Track. Pages 6850-6858. https://doi.org/10.24963/ijcai.2024/757

Incremental scene reconstruction is essential to the navigation in robotics. Most of the conventional methods typically make use of either TSDF (truncated signed distance functions) volume or neural networks to implicitly represent the surface. Due to the voxel representation or involving with time-consuming sampling, they have difficulty in balancing speed, memory storage, and surface quality. In this paper, we propose a novel hybrid voxel-octree approach to effectively fuse octree with voxel structures so that we can take advantage of both implicit surface and explicit triangular mesh representation. Such sparse structure preserves triangular faces in the leaf nodes and produces partial meshes sequentially for incremental reconstruction. This storage scheme allows us to naturally optimize the mesh in explicit 3D space to achieve higher surface quality. We iteratively deform the mesh towards the target and recovers vertex colors by optimizing a shading model. Experimental results on several datasets show that our proposed approach is capable of quickly and accurately reconstructing a scene with realistic colors. Code is available at https://github.com/Frankuzi/HVOFusion
Keywords:
Robotics: ROB: Localization, mapping, state estimation
Computer Vision: CV: 3D computer vision
Computer Vision: CV: Applications
Robotics: ROB: Robotics and vision