gDLS*: Generalized Pose-and-Scale Estimation Given Scale and Gravity Priors
Abstract
Many real-world applications in augmented reality (AR), 3D mapping, and robotics require both fast and accurate estimation of camera poses and scales from multiple images captured by multiple cameras or a single moving camera. Achieving high speed and maintaining high accuracy in a pose-and-scale estimator are often conflicting goals. To simultaneously achieve both, we exploit a priori knowledge about the solution space. We present gDLS*, a generalized-camera-model pose-and-scale estimator that utilizes rotation and scale priors. gDLS* allows an application to flexibly weigh the contribution of each prior, which is important since priors often come from noisy sensors. Compared to state-of-the-art generalized-pose-and-scale estimators (e.g., gDLS), our experiments on both synthetic and real data consistently demonstrate that gDLS* accelerates the estimation process and improves scale and pose accuracy.
Cite
Text
Fragoso et al. "gDLS*: Generalized Pose-and-Scale Estimation Given Scale and Gravity Priors." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020. doi:10.1109/CVPR42600.2020.00228Markdown
[Fragoso et al. "gDLS*: Generalized Pose-and-Scale Estimation Given Scale and Gravity Priors." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020.](https://mlanthology.org/cvpr/2020/fragoso2020cvpr-gdls/) doi:10.1109/CVPR42600.2020.00228BibTeX
@inproceedings{fragoso2020cvpr-gdls,
title = {{gDLS*: Generalized Pose-and-Scale Estimation Given Scale and Gravity Priors}},
author = {Fragoso, Victor and DeGol, Joseph and Hua, Gang},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
year = {2020},
doi = {10.1109/CVPR42600.2020.00228},
url = {https://mlanthology.org/cvpr/2020/fragoso2020cvpr-gdls/}
}