Search Results for author: Johannes L. Schonberger

Found 10 papers, 1 papers with code

Learning to Fuse Proposals from Multiple Scanline Optimizations in Semi-Global Matching

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.

General Classification

Learning Priors for Semantic 3D Reconstruction

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.

3D Reconstruction

VSO: Visual Semantic Odometry

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.

Autonomous Driving Visual Odometry

Structure-From-Motion Revisited

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.

3D Reconstruction Structure from Motion

From Dusk Till Dawn: Modeling in the Dark

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.

Reconstructing the World* in Six Days *(As Captured by the Yahoo 100 Million Image Dataset)

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.

Image Clustering Structure from Motion

PAIGE: PAirwise Image Geometry Encoding for Improved Efficiency in Structure-From-Motion

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.

Structure from Motion

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