We introduce MIPS-Fusion, a robust and scalable online RGB-D reconstruction method based on a novel neural implicit representation -- multi-implicit-submap. Different from existing neural RGB-D reconstruction methods lacking either flexibility with a single neural map or scalability due to extra storage of feature grids, we propose a pure neural representation tackling both difficulties with a divide-and-conquer design. In our method, neural submaps are incrementally allocated alongside the scanning trajectory and efficiently learned with local neural bundle adjustments. The submaps can be refined individually in a back-end optimization and optimized jointly to realize submap-level loop closure. Meanwhile, we propose a hybrid tracking approach combining randomized and gradient-based pose optimizations. For the first time, randomized optimization is made possible in neural tracking with several key designs to the learning process, enabling efficient and robust tracking even under fast camera motions. The extensive evaluation demonstrates that our method attains higher reconstruction quality than the state of the arts for large-scale scenes and under fast camera motions.

翻译：摘要：我们提出MIPS-Fusion，一种基于新型神经隐式表示——多隐式子地图的鲁棒可扩展在线RGB-D重建方法。与现有神经RGB-D重建方法不同（这些方法或因单一神经地图缺乏灵活性，或因额外存储特征网格导致扩展性受限），我们提出一种纯神经表示，通过分治设计同时解决这两类难题。在我们的方法中，神经子地图沿扫描轨迹逐步分配，并通过局部神经束调整高效学习。子地图可在后端优化中单独精炼，并联合优化以实现子地图级闭环检测。同时，我们提出一种结合随机化与梯度位姿优化的混合追踪方法。通过若干关键的学习过程设计，我们首次在神经追踪中实现了随机化优化，即使在快速相机运动下也能实现高效鲁棒的追踪。广泛评估表明，在大规模场景和快速相机运动条件下，我们的方法达到比现有技术更高的重建质量。