Feature matching is a challenging computer vision task that involves finding
correspondences between two images of a 3D scene. In this paper we consider the dense …

We propose-RANSAC, a generalized differentiable RANSAC that allows learning the entire
randomized robust estimation pipeline. The proposed approach enables the use of …

Previous methods solve feature matching and pose estimation using a two-stage process by
first finding matches and then estimating the pose. As they ignore the geometric …

Camera pose estimation for two-view geometry traditionally relies on RANSAC. Normally, a
multitude of image correspondences leads to a pool of proposed hypotheses, which are …

RANSAC-based algorithms are the standard techniques for robust estimation in computer
vision. These algorithms are iterative and computationally expensive; they alternate …

We propose StereoGlue, a method designed for joint feature matching and robust estimation
that effectively reduces the combinatorial complexity of these tasks using single-point …

Robust estimation is a crucial and still challenging task, which involves estimating model
parameters in noisy environments. Although conventional sampling consensus-based …

Visual odometry and visual simultaneous localization and map** aid in tracking the
position of a camera and map** the surroundings using images. It is an important part of …

Camera, and associated with its objects within the field of view, localization could benefit
many computer vision fields, such as autonomous driving, robot navigation, and augmented …

Two-view homography estimation is a classic and fundamental problem in computer vision.
While conceptually simple the problem quickly becomes challenging when multiple planes …