no code implementations • CVPR 2021 • Marcel Geppert, Viktor Larsson, Pablo Speciale, Johannes L. Schonberger, Marc Pollefeys
In this paper, we propose a solution to the uncalibrated privacy preserving localization and mapping problem.
no code implementations • ECCV 2018 • Johannes L. Schonberger, Sudipta N. Sinha, Marc Pollefeys
Semi-Global Matching (SGM) uses an aggregation scheme to combine costs from multiple 1D scanline optimizations that tends to hurt its accuracy in difficult scenarios.
no code implementations • ECCV 2018 • Konstantinos-Nektarios Lianos, Johannes L. Schonberger, Marc Pollefeys, Torsten Sattler
Robust data association is a core problem of visual odometry, where image-to-image correspondences provide constraints for camera pose and map estimation.
no code implementations • ECCV 2018 • Ian Cherabier, Johannes L. Schonberger, Martin R. Oswald, Marc Pollefeys, Andreas Geiger
In contrast to existing variational methods for semantic 3D reconstruction, our model is end-to-end trainable and captures more complex dependencies between the semantic labels and the 3D geometry.
no code implementations • CVPR 2017 • Thomas Schops, Johannes L. Schonberger, Silvano Galliani, Torsten Sattler, Konrad Schindler, Marc Pollefeys, Andreas Geiger
Motivated by the limitations of existing multi-view stereo benchmarks, we present a novel dataset for this task.
1 code implementation • CVPR 2016 • Johannes L. Schonberger, Jan-Michael Frahm
Incremental Structure-from-Motion is a prevalent strategy for 3D reconstruction from unordered image collections.
Ranked #13 on Point Clouds on Tanks and Temples
no code implementations • CVPR 2016 • Filip Radenovic, Johannes L. Schonberger, Dinghuang Ji, Jan-Michael Frahm, Ondrej Chum, Jiri Matas
We present an algorithm that leverages the appearance variety to obtain more complete and accurate scene geometry along with consistent multi-illumination appearance information.
no code implementations • CVPR 2015 • Johannes L. Schonberger, Filip Radenovic, Ondrej Chum, Jan-Michael Frahm
Structure-from-Motion for unordered image collections has significantly advanced in scale over the last decade.
no code implementations • CVPR 2015 • Jared Heinly, Johannes L. Schonberger, Enrique Dunn, Jan-Michael Frahm
We propose a novel, large-scale, structure-from-motion framework that advances the state of the art in data scalability from city-scale modeling (millions of images) to world-scale modeling (several tens of millions of images) using just a single computer.
no code implementations • CVPR 2015 • Johannes L. Schonberger, Alexander C. Berg, Jan-Michael Frahm
Based on the insights of this evaluation, we propose a learning-based approach, the PAirwise Image Geometry Encoding (PAIGE), to efficiently identify image pairs with scene overlap without the need to perform exhaustive putative matching and geometric verification.