In this paper, we propose a solution to the uncalibrated privacy preserving localization and mapping problem.
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.
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.
Robust data association is a core problem of visual odometry, where image-to-image correspondences provide constraints for camera pose and map estimation.
Motivated by the limitations of existing multi-view stereo benchmarks, we present a novel dataset for this task.
We present an algorithm that leverages the appearance variety to obtain more complete and accurate scene geometry along with consistent multi-illumination appearance information.
Structure-from-Motion for unordered image collections has significantly advanced in scale over the last decade.
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.
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.