- 专利标题: Self-supervised 3D keypoint learning for ego-motion estimation
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申请号: US17093360申请日: 2020-11-09
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公开(公告)号: US11900626B2公开(公告)日: 2024-02-13
- 发明人: Jiexiong Tang , Rares A. Ambrus , Vitor Guizilini , Sudeep Pillai , Hanme Kim , Adrien David Gaidon
- 申请人: TOYOTA RESEARCH INSTITUTE, INC.
- 申请人地址: US CA Los Altos
- 专利权人: TOYOTA RESEARCH INSTITUTE, INC.
- 当前专利权人: TOYOTA RESEARCH INSTITUTE, INC.
- 当前专利权人地址: US CA Los Altos
- 代理机构: SEYFARTH SHAW LLP
- 主分类号: G06T7/00
- IPC分类号: G06T7/00 ; G06T7/579 ; B60W60/00 ; G06T7/246 ; G06T7/33 ; G06T7/269 ; G06T7/73 ; G06N3/08 ; G06V10/764 ; G06V10/82 ; G06V10/46 ; G06V20/56 ; G06V20/64
摘要:
A method for learning depth-aware keypoints and associated descriptors from monocular video for ego-motion estimation is described. The method includes training a keypoint network and a depth network to learn depth-aware keypoints and the associated descriptors. The training is based on a target image and a context image from successive images of the monocular video. The method also includes lifting 2D keypoints from the target image to learn 3D keypoints based on a learned depth map from the depth network. The method further includes estimating ego-motion from the target image to the context image based on the learned 3D keypoints.
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