activity
20152026
most citedR2D2: Repeatable and Reliable Detector and Descriptor

356 citations · 582 across the 23 of their papers we have counts for

collaborators

24 papers

cs.CV2026

BLASt3R: Bundle Adjustment of Any Image Set with Multi-View Matching and Monocular Priors

Vincent Leroy, Philippe Weinzaepfel, Lojze Zust +2

Recent hybrid Structure-from-Motion (SfM) systems combine the robustness of feed-forward 3D reconstruction with the accuracy of traditional bundle adjustment (BA) with pixel matchi…

cs.CV2026

Sparse auto-regressive modeling for scene generation from multi-view images

Thomas Lucas, Maxime Pietrantoni, Philippe Weinzaepfel +4

Generating complete 3D scenes from sparse, unconstrained views is a fundamental challenge in 3D vision which requires reasoning beyond observed content while remaining computationa…

cs.CV2026

S-MUSt3R: Sliding Multi-view 3D Reconstruction

Leonid Antsfeld, Boris Chidlovskii, Yohann Cabon +2

The recent paradigm shift in 3D vision led to the rise of foundation models with remarkable capabilities in 3D perception from uncalibrated images. However, extending these models…

cs.CV2025

PanSt3R: Multi-view Consistent Panoptic Segmentation

Lojze Zust, Yohann Cabon, Juliette Marrie +4

Panoptic segmentation of 3D scenes, involving the segmentation and classification of object instances in a dense 3D reconstruction of a scene, is a challenging problem, especially…

cs.CV2025

Pow3R: Empowering Unconstrained 3D Reconstruction with Camera and Scene Priors

Wonbong Jang, Philippe Weinzaepfel, Vincent Leroy +2

We present Pow3r, a novel large 3D vision regression model that is highly versatile in the input modalities it accepts. Unlike previous feed-forward models that lack any mechanism…

cs.CV2025

MUSt3R: Multi-view Network for Stereo 3D Reconstruction

Yohann Cabon, Lucas Stoffl, Leonid Antsfeld +4

DUSt3R introduced a novel paradigm in geometric computer vision by proposing a model that can provide dense and unconstrained Stereo 3D Reconstruction of arbitrary image collection…