[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.


gDLS* is released under the MIT license. Please consider citing this work if you use it.

gDLS* on Github


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