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