@inproceedings{fragoso2020gdls,
author = {Fragoso, Victor and DeGol, Joseph and Hua, Gang},
title = {gDLS*: Generalized Pose-and-Scale Estimation Given Scale and Gravity Priors},
booktitle = {CVPR 2020},
year = {2020},
month = {June},
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.},
url = {http://approjects.co.za/?big=en-us/research/publication/gdls-generalized-pose-and-scale-estimation-given-scale-and-gravity-priors/},
}