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- W4312877043 abstract "While category-level 9DoF object pose estimation has emerged recently, previous correspondence-based or direct regression methods are both limited in accuracy due to the huge intra-category variances in object shape and color, etc. Orthogonal to them, this work presents a category-level object pose and size refiner CATRE, which is able to iteratively enhance pose estimate from point clouds to produce accurate results. Given an initial pose estimate, CATRE predicts a relative transformation between the initial pose and ground truth by means of aligning the partially observed point cloud and an abstract shape prior. In specific, we propose a novel disentangled architecture being aware of the inherent distinctions between rotation and translation/size estimation. Extensive experiments show that our approach remarkably outperforms state-of-the-art methods on REAL275, CAMERA25, and LM benchmarks up to a speed of $${approx }{85.32},{text {Hz}}$$ , and achieves competitive results on category-level tracking. We further demonstrate that CATRE can perform pose refinement on unseen category. Code and trained models are available ( https://github.com/THU-DA-6D-Pose-Group/CATRE.git )." @default.
- W4312877043 created "2023-01-05" @default.
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- W4312877043 date "2022-01-01" @default.
- W4312877043 modified "2023-09-30" @default.
- W4312877043 title "CATRE: Iterative Point Clouds Alignment for Category-Level Object Pose Refinement" @default.
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- W4312877043 doi "https://doi.org/10.1007/978-3-031-20086-1_29" @default.
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