  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.
