Learning Geometry Consistent Neural Radiance Fields from Sparse and Unposed Views

The latest progress in novel view synthesis can be attributed to the Neural Radiance Field (NeRF), which requires densely sampled images with precise camera poses. However, collecting dense input images for a NeRF with accurate camera poses is highly expensive in many real-world scenarios. In this paper, we propose to learn Geometry Consistent Neural Radiance Field (GC-NeRF), to tackle this challenge by jointly optimizing a NeRF and its corresponding camera poses with sparse (as low as 2) and unposed views. First, the proposed GC-NeRF establishes image-level geometric consistencies, by producing photometric constraints from inter- and intra-views to update the NeRF and the camera poses in a fine-grained manner. Then, we adopt geometry projection with camera extrinsic parameters to further provide region-level consistency supervisions, which constructs pseudo-pixel labels to capture critical matching correlations. Moreover, we present an adaptive high-frequency mapping function to augment the geometry and texture information of the 3D scene. Extensive experiments on multiple challenging real-world datasets validate the effectiveness of the proposed GC-NeRF, which sets a new state-of-the-art for effectively learning NeRF with sparse and unposed views.