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- W4317552994 abstract "We propose a new method for object pose estimation without CAD models. The previous feature-matching-based method OnePose has shown promising results under a one-shot setting which eliminates the need for CAD models or object-specific training. However, OnePose relies on detecting repeatable image keypoints and is thus prone to failure on low-textured objects. We propose a keypoint-free pose estimation pipeline to remove the need for repeatable keypoint detection. Built upon the detector-free feature matching method LoFTR, we devise a new keypoint-free SfM method to reconstruct a semi-dense point-cloud model for the object. Given a query image for object pose estimation, a 2D-3D matching network directly establishes 2D-3D correspondences between the query image and the reconstructed point-cloud model without first detecting keypoints in the image. Experiments show that the proposed pipeline outperforms existing one-shot CAD-model-free methods by a large margin and is comparable to CAD-model-based methods on LINEMOD even for low-textured objects. We also collect a new dataset composed of 80 sequences of 40 low-textured objects to facilitate future research on one-shot object pose estimation. The supplementary material, code and dataset are available on the project page: https://zju3dv.github.io/onepose_plus_plus/." @default.
- W4317552994 created "2023-01-20" @default.
- W4317552994 creator A5056972984 @default.
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- W4317552994 date "2023-01-18" @default.
- W4317552994 modified "2023-10-17" @default.
- W4317552994 title "OnePose++: Keypoint-Free One-Shot Object Pose Estimation without CAD Models" @default.
- W4317552994 doi "https://doi.org/10.48550/arxiv.2301.07673" @default.
- W4317552994 hasPublicationYear "2023" @default.
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