An Efficient Volumetric Mesh Representation for Real-time Scene Reconstruction using Spatial Hashing
This work addresses the need for compact and fast mesh representation in robotics and real-time applications, though it appears incremental as it builds on existing volumetric methods.
The paper tackles the problem of efficient online mesh storage and manipulation for real-time scene reconstruction by proposing a novel framework using spatial hashing and Hamming distance refinement, resulting in significantly reduced memory consumption while maintaining running speed.
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.