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We propose a generalizable neural radiance fields - MonoNeRF, that can betrained on large-scale monocular videos of moving in static scenes without anyground-truth annotations of depth and camera poses. MonoNeRF follows anAutoencoder-based architecture, where the encoder estimates the monocular depthand the camera pose, and the decoder constructs a Multiplane NeRFrepresentation based on the depth encoder feature, and renders the input frameswith the estimated camera. The learning is supervised by the reconstructionerror. Once the model is learned, it can be applied to multiple applicationsincluding depth estimation, camera pose estimation, and single-image novel viewsynthesis. More qualitative results are available at:https://oasisyang.github.io/mononerf .

MonoNeRF: Learning Generalizable NeRFs from Monocular Videos without Camera Pose