PoseDiffusion: solving pose estimation via diffusion-aided bundle adjustment by Wang, J, Rupprecht, C, Novotny, D

“…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. …”

Efficient online structured output learning for keypoint-based object tracking by Hare, S, Saffari, A, Torr, PHS

“…These approaches often model an object as a collection of keypoints and associated descriptors, and detection then involves first constructing a set of correspondences between object and image keypoints via descriptor matching, and subsequently using these correspondences as input to a robust geometric estimation algorithm such as RANSAC to find the transformation of the object in the image. …”

That's my point: compact object-centric LiDAR pose estimation for large-scale outdoor localisation by Pramatarov, G, Gadd, M, Newman, P, De Martini, D

“…The respective matches allow us to recover the relative transformation between scans through weighted Singular Value Decomposition (SVD) and RANdom SAmple Consensus (RANSAC). We demonstrate that such representation is sufficient for metric localisation by registering point clouds taken under different viewpoints on the KITTI dataset, and at different periods of time localising between KITTI and KITTI-360. …”

Random forests versus neural networks - What's best for camera localization? by Massiceti, D, Krull, A, Brachmann, E, Rother, C, Torr, P

“…State-of-the-art approaches accomplish this in two steps: firstly, regressing for every pixel in the image its 3D scene coordinate and subsequently, using these coordinates to estimate the final 6D camera pose via RANSAC. To solve the first step, Random Forests (RFs) are typically used. …”