FlowCam: Training Generalizable 3D Radiance Fields without Camera Poses
  via Pixel-Aligned Scene Flow
Cameron Smith Yilun Du Ayush Tewari Vincent Sitzmann
Reconstruction of 3D neural fields from posed images has emerged as a
promising method for self-supervised representation learning. The key challenge
preventing the deployment of these 3D scene learners on large-scale video data
is their dependence on precise camera poses from structure-from-motion, which
is prohibitively expensive to run at scale. We propose a method that jointly
reconstructs camera poses and 3D neural scene representations online and in a
single forward pass. We estimate poses by first lifting frame-to-frame optical
flow to 3D scene flow via differentiable rendering, preserving locality and
shift-equivariance of the image processing backbone. SE(3) camera pose
estimation is then performed via a weighted least-squares fit to the scene flow
field. This formulation enables us to jointly supervise pose estimation and a
generalizable neural scene representation via re-rendering the input video, and
thus, train end-to-end and fully self-supervised on real-world video datasets.
We demonstrate that our method performs robustly on diverse, real-world video,
notably on sequences traditionally challenging to optimization-based pose
estimation techniques.