[Joseph DeGol]

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

Victor Fragoso, Joseph DeGol, Gang Hua

2020 Conference on Computer Vision and Pattern Recognition (CVPR '20)






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.


We are currently preparing the code for release. Check back soon!


We are currently preparing the data for release. Check back soon!


Because of corona virus, CVPR 2020 was a virtual conference. Below are extra presentation materials that we created in accordance with the virtual conference format.

Poster Video


  author    = {Victor Fragoso and Joseph DeGol and Gang Hua},
  title     = {gDLS*: Generalized Pose-and-Scale Estimation Given Scale and Gravity Priors},
  booktitle = {CVPR},
  year      = {2020}

Last Updated: 10/21/18