Mesh plays an indispensable role in dense real-time reconstruction essential
in robotics. Efforts have been made to maintain flexible data structures for 3D
data fusion, yet an efficient incremental framework specifically designed for
online mesh storage and manipulation is missing. We propose a novel framework
to compactly generate, update, and refine mesh for scene reconstruction upon a
volumetric representation. Maintaining a spatial-hashed field of cubes, we
distribute vertices with continuous value on discrete edges that support O(1)
vertex accessing and forbid memory redundancy. By introducing Hamming distance
in mesh refinement, we further improve the mesh quality regarding the triangle
type consistency with a low cost. Lock-based and lock-free operations were
applied to avoid thread conflicts in GPU parallel computation. Experiments
demonstrate that the mesh memory consumption is significantly reduced while the
running speed is kept in the online reconstruction process.

本文提出了一种创新的框架，采用体积表示法对网格进行紧缩、更新和细化，同时采用哈希方法实现 O (1) 的访问，引入 Hamming 距离方法进一步改善三角形一致性，同时使用基于锁和无锁操作实现 GPU 并行计算，实验证明该方法在在线重建过程中能够显著减少网格内存占用且速度快。