Arbitrary Large-Scale Scene Reconstruction without Annotated Block Partitions

Large-scale scene reconstruction is a challenging problem. As different parts of the scene could be visible from different collected image frames, previous works manually use distance or geography to decompose the scene into parts and reconstruct each part of the scene separately. However, such manual decomposition is a laborious and time-consuming task when applied to large-scale scene reconstruction in real-world applications. To address this, we propose VisibleNeRF automatically reconstructs large-scale scenes by decomposing scenes into parts based on the part visibility. More specifically, we propose a visibility judgment strategy to decompose the scenes into visible and invisible parts. Then we reconstruct the visible part with the corresponding collected images and continue to decompose the rest of the invisible parts with the proposed visibility judgment strategy. New NeRF modules are re-established for the decomposed invisible parts until the entire scene is reconstructed. To the best of our knowledge, we are the first to propose an online reconstruction of large-scale scenes without manual decomposition. Experimental results on three datasets show that our method successfully reconstructs large-scale scenes in a fully automatic manner. Besides, in the widely used Mission Bay dataset, our model outperforms other state-of-the-art methods by a large margin.