Jianyuan Wang
Description: 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.
3d reconstruction (45) posediffusion (1) camera pose estimation (1)
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
We propose to formulate the Structure from Motion (SfM) problem inside a probabilistic diffusion framework. Training is supervised given a multi-view datasets of images and camera poses to learn a diffusion model \(D_\theta\) to model \(p(x |I)\). During inference, the reverse diffusion process is guided by optimizing the geometric consistency between poses via Sampson Epipolar Error.
We provide the qualitative samples of pose estimation on the CO3Dv2 dataset. Given input images I (first row), our PoseDiffusion (2nd row) is compared to RelPose (3rd row), COLMAP+SPSG (4th row), and the ground truth . Missing cameras indicate failure.
Jianyuan Wang