MLESAC: a new robust estimator with application to estimating image geometry by Torr, PHS, Zisserman, A

“…The first is a new robust estimator MLESAC which is a generalization of the RANSAC estimator. It adopts the same sampling strategy as RANSAC to generate putative solutions, but chooses the solution that maximizes the likelihood rather than just the number of inliers. …”

IMPSAC: synthesis of importance sampling and random sample consensus by Torr, PHS, Davidson, C

“…It is shown that the method is superior to previous single resolution RANSAC-style feature matchers.…”

An integrated Bayesian approach to layer extraction from image sequences by Torr, PHS, Szeliski, R, Anandan, P

“…In order to achieve the optimization, a Bayesian version of RANSAC is developed with which to initialize the segmentation. …”

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. …”

What Could Move? Finding Cars, Pedestrians and Bicyclists in 3D Laser Data by Wang, D, Posner, I, Newman, P, IEEE

“…While our system is agnostic to the specific clustering algorithm deployed we explore the use of a Euclidean Minimum Spanning Tree for an end-to-end segmentation pipeline and devise a RANSAC-based edge selection criterion. © 2012 IEEE.…”

Scalable Active Matching by Handa, A, Chli, M, Strasdat, H, Davison, A, IEEE

“…While these priors are often partially used post-hoc to resolve matching consensus in algorithms like RANSAC, it was recently shown that fully integrating them in an 'Active Matching' (AM) approach permits efficient guided image processing with rigorous decisions guided by Information Theory. …”

Robust detection of degenerate configurations while estimating the fundamental matrix by Torr, PHS, Zisserman, A, Maybank, SJ

“…The method is called PLUNDER-DL and is a generalization of the robust estimator RANSAC.</p> <p>The method is evaluated on many differing pairs of real images. …”

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. …”

Towards real-time forest inventory using handheld LiDAR by Proudman, A, Ramezani, M, Digumarti, ST, Chebrolu, N, Fallon, M

“…The DBH is estimated online by fitting a cylinder to each tree trunk through a least-squares optimization within a RANSAC loop. We demonstrate our mapping approach operating in two different forests (both ecological and commercial) with the total travel distance spanning several kilometres. …”

Real-time RGB-D camera pose estimation in novel scenes using a relocalisation cascade by Cavallari, T, Golodetz, S, Lord, NA, Valentin, J, Prisacariu, VA, Di Stefano, L, Torr, PHS

“…To achieve this, we make several changes to the original approach: (i) instead of simply accepting the camera pose hypothesis produced by RANSAC without question, we make it possible to score the final few hypotheses it considers using a geometric approach and select the most promising one; (ii) we chain several instantiations of our relocaliser (with different parameter settings) together in a cascade, allowing us to try faster but less accurate relocalisation first, only falling back to slower, more accurate relocalisation as necessary; and (iii) we tune the parameters of our cascade, and the individual relocalisers it contains, to achieve effective overall performance. …”