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.

翻译：增量式场景重建对机器人导航至关重要。传统方法通常利用TSDF（截断符号距离函数）体素或神经网络隐式表示表面。由于体素表示或涉及耗时的采样，这些方法难以在速度、内存存储和表面质量之间取得平衡。本文提出了一种新颖的体素-八叉树混合方法，通过有效融合八叉树与体素结构，可同时利用隐式表面和显式三角网格表示的优点。该稀疏结构在叶节点中保存三角面片，并顺序生成局部网格以实现增量式重建。这种存储方案允许我们在显式三维空间中自然优化网格，从而获得更高的表面质量。通过迭代形变使网格逼近目标，并基于光照模型优化恢复顶点颜色。多个数据集上的实验结果表明，本方法能够快速精确地重建具有真实色彩的场景。