HVOFusion: Incremental Mesh Reconstruction Using Hybrid Voxel Octree

TL;DR

HVOFusion introduces a hybrid voxel-octree method for incremental scene reconstruction that balances speed, memory, and surface quality by combining implicit and explicit surface representations.

Contribution

It presents a novel hybrid voxel-octree approach that fuses voxel and octree structures for efficient, incremental mesh reconstruction with improved surface quality.

Findings

Capable of quick scene reconstruction

Achieves high surface quality with realistic colors

Balances speed and memory efficiency

Abstract

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.