NeRFusion: Fusing Radiance Fields for Large-Scale Scene Reconstruction
It addresses the problem of efficient and realistic rendering for large-scale indoor scenes, representing an incremental improvement over existing methods.
The paper tackles the challenge of large-scale scene reconstruction by combining NeRF and TSDF-based fusion, achieving state-of-the-art quality with real-time reconstruction at 22 fps.
While NeRF has shown great success for neural reconstruction and rendering, its limited MLP capacity and long per-scene optimization times make it challenging to model large-scale indoor scenes. In contrast, classical 3D reconstruction methods can handle large-scale scenes but do not produce realistic renderings. We propose NeRFusion, a method that combines the advantages of NeRF and TSDF-based fusion techniques to achieve efficient large-scale reconstruction and photo-realistic rendering. We process the input image sequence to predict per-frame local radiance fields via direct network inference. These are then fused using a novel recurrent neural network that incrementally reconstructs a global, sparse scene representation in real-time at 22 fps. This global volume can be further fine-tuned to boost rendering quality. We demonstrate that NeRFusion achieves state-of-the-art quality on both large-scale indoor and small-scale object scenes, with substantially faster reconstruction than NeRF and other recent methods.