6D Object Pose Estimation in Space

 

Estimating the relative 6D pose of an object, that is, its 3 rotations and 3 translations with respect to the camera from a single image is crucial in many applications.

Most traditional methods address this problem by first detecting keypoints in the input image, then establishing 3D-to-2D correspondences, and finally running a Perspective-n-Point algorithm. Unfortunately, this often fails in the presence of severe occlusions and cluttered backgrounds.

To improve robustness, we have several end-to-end trainable networks depicted that yield reliable and scale-insensitive 6D poses. These will be tested in harsh spatial conditions in the context of the ClearSpace-1 project, whose chaser satellite will be launched to retrieve and de-orbit a non-operational satellite in 2025.

Software:
https://github.com/cvlab-epfl/wide-depth-range-pose
https://github.com/cvlab-epfl/single-stage-pose
https://github.com/cvlab-epfl/segmentation-driven-pose

Wide-Depth-Range 6D Object Pose Estimation in Space

Y. Hu; S. Speierer; W. Jakob; P. Fua; M. Salzmann 

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

2021-06-25

Conference on Computer Vision and Pattern Recognition (CVPR), Virtual, June 19-25, 2021.

p. 15870-15879

DOI : 10.1109/CVPR46437.2021.01561

Single-Stage 6D Object Pose Estimation

Y. Hu; P. Fua; W. Wang; M. Salzmann 

2020 Ieee/Cvf Conference On Computer Vision And Pattern Recognition (Cvpr)

2020-06-16

Computer Vision and Pattern Recognition (CVPR), Seattle, Washington, June 16 – 18, 2020.

p. 2927-2936

DOI : 10.1109/CVPR42600.2020.00300

Segmentation-driven 6D Object Pose Estimation

Y. Hu; J. L. Hugonot; P. Fua; M. Salzmann 

2019 Ieee/Cvf Conference On Computer Vision And Pattern Recognition (Cvpr 2019)

2019-06-16

Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, California, USA, June 16-20, 2019.

p. 3380-3389

DOI : 10.1109/CVPR.2019.00350