29 citations · 49 across the 10 of their papers we have counts for
13 papers · 1 filter
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…
Single-Image Depth Prediction Makes Feature Matching Easier
Carl Toft, Daniyar Turmukhambetov, Torsten Sattler +2
Good local features improve the robustness of many 3D re-localization and multi-view reconstruction pipelines. The problem is that viewing angle and distance severely impact the re…
Learning Stereo from Single Images
Jamie Watson, Oisin Mac Aodha, Daniyar Turmukhambetov +2
Supervised deep networks are among the best methods for finding correspondences in stereo image pairs. Like all supervised approaches, these networks require ground truth data duri…
Image Stylization for Robust Features
Iaroslav Melekhov, Gabriel J. Brostow, Juho Kannala +1
Local features that are robust to both viewpoint and appearance changes are crucial for many computer vision tasks. In this work we investigate if photorealistic image stylization…
Predicting Visual Overlap of Images Through Interpretable Non-Metric Box Embeddings
Anita Rau, Guillermo Garcia-Hernando, Danail Stoyanov +2
To what extent are two images picturing the same 3D surfaces? Even when this is a known scene, the answer typically requires an expensive search across scale space, with matching a…
Self-Supervised Monocular Depth Hints
Jamie Watson, Michael Firman, Gabriel J. Brostow +1
Monocular depth estimators can be trained with various forms of self-supervision from binocular-stereo data to circumvent the need for high-quality laser scans or other ground-trut…
Interpretable Transformations with Encoder-Decoder Networks
Daniel E. Worrall, Stephan J. Garbin, Daniyar Turmukhambetov +1
Deep feature spaces have the capacity to encode complex transformations of their input data. However, understanding the relative feature-space relationship between two transformed…
Harmonic Networks: Deep Translation and Rotation Equivariance
Daniel E. Worrall, Stephan J. Garbin, Daniyar Turmukhambetov +1
Translating or rotating an input image should not affect the results of many computer vision tasks. Convolutional neural networks (CNNs) are already translation equivariant: input…