1 citations · 2 across the 8 of their papers we have counts for
10 papers · 1 filter
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…