Incremental 3D Reconstruction through a Hybrid Explicit-and-Implicit Representation

3D reconstruction is an important task in computer vision and is widely used in robotics and autonomous driving. When building large-scale scenes, limitations in computing resources and the difficulty of accessing the entire dataset in a single task are inevitable. Therefore, an incremental reconstruction approach is desired. On the one hand, traditional explicit 3D reconstruction methods such as SLAM and SFM require global optimization, which means that time and space resources increase dramatically with the growth of training data. On the other hand, implicit methods like Neural Radiation Fields (NeRF) suffer from catastrophic forgetting if trained incremen- tally. In this paper, we incrementally reconstruct 3D models in a hybrid representation, where the density of the radiation field is formulated by a voxel grid, and the view-dependent color information of the points is inferred by a shallow MLP. The expansion of the voxel grid and the distillation of the shallow MLP are efficient in this case. Experimental results demonstrate that our incremental method achieves a level of accuracy on par with approaches employing global optimization techniques.