CF-NeRF: Camera Parameter Free Neural Radiance Fields with Incremental Learning
This addresses the limitation of existing NeRF methods in handling camera rotations for novel view synthesis, offering a more robust solution for 3D reconstruction tasks.
The paper tackles the problem of Neural Radiance Fields (NeRF) relying on pre-computed camera parameters, which fails in rotation scenarios, by proposing CF-NeRF, a camera parameter-free method that incrementally reconstructs 3D scenes and recovers parameters, achieving state-of-the-art results on the challenging NeRFBuster dataset with 12 scenes under complex trajectories.
Neural Radiance Fields (NeRF) have demonstrated impressive performance in novel view synthesis. However, NeRF and most of its variants still rely on traditional complex pipelines to provide extrinsic and intrinsic camera parameters, such as COLMAP. Recent works, like NeRFmm, BARF, and L2G-NeRF, directly treat camera parameters as learnable and estimate them through differential volume rendering. However, these methods work for forward-looking scenes with slight motions and fail to tackle the rotation scenario in practice. To overcome this limitation, we propose a novel \underline{c}amera parameter \underline{f}ree neural radiance field (CF-NeRF), which incrementally reconstructs 3D representations and recovers the camera parameters inspired by incremental structure from motion (SfM). Given a sequence of images, CF-NeRF estimates the camera parameters of images one by one and reconstructs the scene through initialization, implicit localization, and implicit optimization. To evaluate our method, we use a challenging real-world dataset NeRFBuster which provides 12 scenes under complex trajectories. Results demonstrate that CF-NeRF is robust to camera rotation and achieves state-of-the-art results without providing prior information and constraints.