most citedMapAnything: Universal Feed-Forward Metric 3D Reconstruction

1 citations · 1 across the 4 of their papers we have counts for

collaborators

5 papers

cs.CV2026

Lens Flare Removal and Reconstruction

Tarun Yenamandra, Jonathon Luiten, Daniel Cremers +1

The presence of lens flares in images can significantly reduce the quality of downstream application results for tasks such as 3D scene reconstruction. This is because lens flares…

cs.CV2026

DRoPS: Dynamic 3D Reconstruction of Pre-Scanned Objects

Narek Tumanyan, Samuel Rota Bulò, Denis Rozumny +5

Dynamic scene reconstruction from casual videos has seen recent remarkable progress. Numerous approaches have attempted to overcome the ill-posedness of the task by distilling prio…

cs.CV2025★ 1 cited

MapAnything: Universal Feed-Forward Metric 3D Reconstruction

Nikhil Keetha, Norman Müller, Johannes Schönberger +14

We introduce MapAnything, a unified transformer-based feed-forward model that ingests one or more images along with optional geometric inputs such as camera intrinsics, poses, dept…

cs.GR2025

BulletGen: Improving 4D Reconstruction with Bullet-Time Generation

Denis Rozumny, Jonathon Luiten, Numair Khan +2

Transforming casually captured, monocular videos into fully immersive dynamic experiences is a highly ill-posed task, and comes with significant challenges, e.g., reconstructing un…

cs.CV2025

FlowR: Flowing from Sparse to Dense 3D Reconstructions

Tobias Fischer, Samuel Rota Bulò, Yung-Hsu Yang +7

3D Gaussian splatting enables high-quality novel view synthesis (NVS) at real-time frame rates. However, its quality drops sharply as we depart from the training views. Thus, dense…