PoseDiffusion: Solving Pose Estimation via Diffusion-aided Bundle
  Adjustment
Authors: name: Jianyuan Wang, name: Christian Rupprecht, name: David Novotny

Camera pose estimation is a long-standing computer vision problem that to
date often relies on classical methods, such as handcrafted keypoint matching,
RANSAC and bundle adjustment. In this paper, we propose to formulate the
Structure from Motion (SfM) problem inside a probabilistic diffusion framework,
modelling the conditional distribution of camera poses given input images. This
novel view of an old problem has several advantages. (i) The nature of the
diffusion framework mirrors the iterative procedure of bundle adjustment. (ii)
The formulation allows a seamless integration of geometric constraints from
epipolar geometry. (iii) It excels in typically difficult scenarios such as
sparse views with wide baselines. (iv) The method can predict intrinsics and
extrinsics for an arbitrary amount of images. We demonstrate that our method
PoseDiffusion significantly improves over the classic SfM pipelines and the
learned approaches on two real-world datasets. Finally, it is observed that our
method can generalize across datasets without further training. Project page:
https://posediffusion.github.io/