no code implementations • 16 Nov 2023 • Yangzheng Wu, Michael Greenspan
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.
1 code implementation • 15 Aug 2023 • Yangzheng Wu, Michael Greenspan
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.
1 code implementation • 14 Oct 2022 • Yangzheng Wu, Alireza Javaheri, Mohsen Zand, Michael Greenspan
We propose a novel keypoint voting 6DoF object pose estimation method, which takes pure unordered point cloud geometry as input without RGB information.
1 code implementation • 6 Apr 2021 • Yangzheng Wu, Mohsen Zand, Ali Etemad, Michael Greenspan
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)