15 citations · 20 across the 9 of their papers we have counts for
14 papers
AutoCompass: Accurate Visual Localization on Public Maps by Learning from Weak Labels
Javier Tirado-Garín, Alan Savio Paul, Shuai Chen +5
Neural map matchers estimate an image's 3-DoF pose relative to a 2D map. These models are trained on large-scale datasets of geo-referenced images, whose position and heading label…
Scene Coordinate Reconstruction: Posing of Image Collections via Incremental Learning of a Relocalizer
Eric Brachmann, Jamie Wynn, Shuai Chen +4
We address the task of estimating camera parameters from a set of images depicting a scene. Popular feature-based structure-from-motion (SfM) tools solve this task by incremental r…
Two-View Geometry Scoring Without Correspondences
Axel Barroso-Laguna, Eric Brachmann, Victor Adrian Prisacariu +2
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 then s…
DiffusioNeRF: Regularizing Neural Radiance Fields with Denoising Diffusion Models
Jamie Wynn, Daniyar Turmukhambetov
Under good conditions, Neural Radiance Fields (NeRFs) have shown impressive results on novel view synthesis tasks. NeRFs learn a scene's color and density fields by minimizing the…
Map-free Visual Relocalization: Metric Pose Relative to a Single Image
Eduardo Arnold, Jamie Wynn, Sara Vicente +5
Can we relocalize in a scene represented by a single reference image? Standard visual relocalization requires hundreds of images and scale calibration to build a scene-specific 3D…
Learning to Predict Repeatability of Interest Points
Anh-Dzung Doan, Daniyar Turmukhambetov, Yasir Latif +2
Many robotics applications require interest points that are highly repeatable under varying viewpoints and lighting conditions. However, this requirement is very challenging as the…