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Found 10 papers, 1 papers with code
PAIGE: PAirwise Image Geometry Encoding for Improved Efficiency in Structure-From-Motion
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.
Reconstructing the World* in Six Days *(As Captured by the Yahoo 100 Million Image Dataset)
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.
From Single Image Query to Detailed 3D Reconstruction
Structure-from-Motion for unordered image collections has significantly advanced in scale over the last decade.
From Dusk Till Dawn: Modeling in the Dark
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 Revisited
Incremental Structure-from-Motion is a prevalent strategy for 3D reconstruction from unordered image collections.
Ranked #13 on
Point Clouds
on Tanks and Temples
A Multi-View Stereo Benchmark With High-Resolution Images and Multi-Camera Videos
Motivated by the limitations of existing multi-view stereo benchmarks, we present a novel dataset for this task.
Learning to Fuse Proposals from Multiple Scanline Optimizations in Semi-Global Matching
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.
VSO: Visual Semantic Odometry
Robust data association is a core problem of visual odometry, where image-to-image correspondences provide constraints for camera pose and map estimation.
Learning Priors for Semantic 3D Reconstruction
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.
Privacy Preserving Localization and Mapping From Uncalibrated Cameras
In this paper, we propose a solution to the uncalibrated privacy preserving localization and mapping problem.
