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Neural Radiance Fields (NeRF) have demonstrated impressive performance innovel view synthesis. However, NeRF and most of its variants still rely ontraditional complex pipelines to provide extrinsic and intrinsic cameraparameters, such as COLMAP. Recent works, like NeRFmm, BARF, and L2G-NeRF,directly treat camera parameters as learnable and estimate them throughdifferential volume rendering. However, these methods work for forward-lookingscenes with slight motions and fail to tackle the rotation scenario inpractice. To overcome this limitation, we propose a novel \underline{c}ameraparameter \underline{f}ree neural radiance field (CF-NeRF), which incrementallyreconstructs 3D representations and recovers the camera parameters inspired byincremental structure from motion (SfM). Given a sequence of images, CF-NeRFestimates the camera parameters of images one by one and reconstructs the scenethrough initialization, implicit localization, and implicit optimization. Toevaluate our method, we use a challenging real-world dataset NeRFBuster whichprovides 12 scenes under complex trajectories. Results demonstrate that CF-NeRFis robust to camera rotation and achieves state-of-the-art results withoutproviding prior information and constraints.

CF-NeRF: Camera Parameter Free Neural Radiance Fields with Incremental Learning