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：我们提出了MIPS-Fusion，一种基于新型神经隐式表示——多隐式子地图的鲁棒且可扩展的在线RGB-D重建方法。与现有神经RGB-D重建方法不同（这些方法受限于单张神经地图缺乏灵活性，或因额外存储特征网格而缺乏可扩展性），我们提出了一种纯神经表示，通过分而治之的设计同时解决了这两个难题。在我们的方法中，神经子地图沿扫描轨迹增量式分配，并通过局部神经束调整高效学习。子地图可在后端优化中独立优化，并通过联合优化实现子地图级别的闭环检测。同时，我们提出了一种结合随机化和基于梯度的位姿优化的混合跟踪方法。首次通过多项关键设计使随机优化在神经跟踪中成为可能，即使面对快速相机运动也能实现高效鲁棒的跟踪。广泛评估表明，在大型场景和快速相机运动条件下，我们的方法取得了优于现有技术的重建质量。