gDLS*: Generalized Pose-and-Scale Estimation Given Scale and Gravity Priors

Victor Fragoso, Joseph DeGol, Gang Hua; The IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020, pp. 2210-2219

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.

Related Material


[pdf] [supp]
[bibtex]
@InProceedings{Fragoso_2020_CVPR,
author = {Fragoso, Victor and DeGol, Joseph and Hua, Gang},
title = {gDLS*: Generalized Pose-and-Scale Estimation Given Scale and Gravity Priors},
booktitle = {The IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2020}
}