  We propose a generalizable neural radiance fields - MonoNeRF, that can be
trained on large-scale monocular videos of moving in static scenes without any
ground-truth annotations of depth and camera poses. MonoNeRF follows an
Autoencoder-based architecture, where the encoder estimates the monocular depth
and the camera pose, and the decoder constructs a Multiplane NeRF
representation based on the depth encoder feature, and renders the input frames
with the estimated camera. The learning is supervised by the reconstruction
error. Once the model is learned, it can be applied to multiple applications
including depth estimation, camera pose estimation, and single-image novel view
synthesis. More qualitative results are available at:
https://oasisyang.github.io/mononerf .
