Daniel Barath

Kilometer-scale 3D reconstructions are now drift-free by closing the loop with SE(3) pose optimization, bypassing Sim(3) limitations.

This work introduces a benchmark of 20 real scenes, from coherent streams to violent splashes, captured by seven synchronized, calibrated 4K cameras at 60 fps, with manually refined per-view liquid and container masks and fixed evaluation splits, and presents SplashSplat, built on a single principle: impose physical structure only where the observations can constrain it.

Achieving rich 3D structures without sacrificing runtime, this framework redefines the limits of Bundle Adjustment in computer vision.

GenRec achieves unparalleled reconstruction fidelity while enhancing perceptual quality in novel view synthesis by intelligently separating reconstruction from generation.

LangLoc achieves unprecedented accuracy in indoor localization from natural language, closing the gap between coarse scene retrieval and precise pose estimation.

AugSplat boosts reconstruction quality in sparse-view 3D vision by leveraging synthetic views from neural radiance fields, achieving real-time performance without sacrificing accuracy.

Achieving accurate pose estimation with rolling shutter cameras using just seven correspondences could revolutionize real-time applications in consumer devices.

A compact scene graph representation allows for efficient visual localization, slashing storage needs while preserving accuracy in complex environments.

Unlock interactive digital twins from messy, real-world videos: FunRec automatically turns egocentric RGB-D recordings into simulation-ready 3D scenes.

DROID-SLAM achieves robust real-time RGB SLAM in dynamic environments by explicitly modeling per-pixel uncertainty, outperforming existing methods that struggle with unknown dynamic objects and cluttered scenes.