  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.
