1 citations · 2 across the 8 of their papers we have counts for
11 papers
Emergent Multi-View Geometry Through Self-Distillation
David Nordström, Thibaut Loiseau, Vincent Lepetit +3
Over a century ago, Henri Poincaré argued that a motionless observer cannot acquire the notion of space. Yet, most visual representation learning methods operate on individual imag…
RoMa-: What Feed-Forward 3D Models Know About Image Matching
David Nordström, Xinyue Zhang, Thibaut Loiseau +2
Learned image matching has experienced significant progress in recent years, culminating in robust and accurate matchers such as RoMa, whose robustness is often attributed to its u…
Who Handles Orientation? Investigating Invariance in Feature Matching
David Nordström, Johan Edstedt, Fredrik Kahl +1
Finding matching keypoints between images is a core problem in 3D computer vision. However, modern matchers struggle with large in-plane rotations. A straightforward mitigation is…
LoMa: Local Feature Matching Revisited
David Nordström, Johan Edstedt, Georg Bökman +6
Local feature matching has long been a fundamental component of 3D vision systems such as Structure-from-Motion (SfM), yet progress has lagged behind the rapid advances of modern d…
MuM: Multi-View Masked Image Modeling for 3D Vision
David Nordström, Johan Edstedt, Fredrik Kahl +1
Self-supervised learning on images seeks to extract meaningful visual representations from unlabeled data. When scaled to large datasets, this paradigm has achieved state-of-the-ar…
RoMa v2: Harder Better Faster Denser Feature Matching
Johan Edstedt, David Nordström, Yushan Zhang +7
Dense feature matching aims to estimate all correspondences between two images of a 3D scene and has recently been established as the gold standard due to its high accuracy and rob…
Quick ViTs: Speeding up Vision Transformers through Equivariance
David Nordström, Johan Edstedt, Fredrik Kahl +1
Natural images exhibit strong geometric regularities: local structures, such as edges, corners, and textures, appear in many orientations and mirror configurations. Since Vision Tr…
A Framework for Reducing the Complexity of Geometric Vision Problems and its Application to Two-View Triangulation with Approximation Bounds
Felix Rydell, Georg Bökman, Fredrik Kahl +1
In this paper, we present a new framework for reducing the computational complexity of geometric vision problems through targeted reweighting of the cost functions used to minimize…
Flopping for FLOPs: Leveraging equivariance for computational efficiency
Georg Bökman, David Nordström, Fredrik Kahl
Incorporating geometric invariance into neural networks enhances parameter efficiency but typically increases computational costs. This paper introduces new equivariant neural netw…
Affine steerers for structured keypoint description
Georg Bökman, Johan Edstedt, Michael Felsberg +1
We propose a way to train deep learning based keypoint descriptors that makes them approximately equivariant for locally affine transformations of the image plane. The main idea is…
Steerers: A framework for rotation equivariant keypoint descriptors
Georg Bökman, Johan Edstedt, Michael Felsberg +1
Image keypoint descriptions that are discriminative and matchable over large changes in viewpoint are vital for 3D reconstruction. However, descriptions output by learned descripto…