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Found 4 papers, 3 papers with code
Pseudo-keypoint RKHS Learning for Self-supervised 6DoF Pose Estimation
This paper addresses the simulation-to-real domain gap in 6DoF PE, and proposes a novel self-supervised keypoint radial voting-based 6DoF PE framework, effectively narrowing this gap using a learnable kernel in RKHS.
Learning Better Keypoints for Multi-Object 6DoF Pose Estimation
We address the problem of keypoint selection, and find that the performance of 6DoF pose estimation methods can be improved when pre-defined keypoint locations are learned, rather than being heuristically selected as has been the standard approach.
Keypoint Cascade Voting for Point Cloud Based 6DoF Pose Estimation
We propose a novel keypoint voting 6DoF object pose estimation method, which takes pure unordered point cloud geometry as input without RGB information.
Vote from the Center: 6 DoF Pose Estimation in RGB-D Images by Radial Keypoint Voting
We propose a novel keypoint voting scheme based on intersecting spheres, that is more accurate than existing schemes and allows for fewer, more disperse keypoints.
Ranked #1 on
6D Pose Estimation using RGBD
on YCB-Video
(ADDS AUC metric)
