PoseDiffusion: Solving Pose Estimation via Diffusion-aided Bundle
  Adjustment
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/
649b98d32a8d0042c3001ad6
Jianyuan Wang
claimed_verified
2023-06-28T20:16:03.061Z
649bf403fd9cea8366d669ad
/avatars/27bd8ca9a948ec38fee950b64f669ce3.svg
Jianyuan Wang
user
JianyuanWang
649b98d32a8d0042c3001ad7
Christian Rupprecht
649b98d32a8d0042c3001ad8
David Novotny
PoseDiffusion: Solving Pose Estimation via Diffusion-aided Bundle
  Adjustment
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/
Camera pose estimation using a diffusion framework improves upon traditional SfM methods by incorporating geometric constraints and generalizing across datasets.
diffusion framework Structure from Motion SfM camera poses epipolar geometry intrinsics extrinsics