Fast 6D Pose Estimation Using Hierarchical Pose Trees. Konishi, Y., Hanzawa, Y., Kawade, M., & Hashimoto, M. Eccv, 2016.
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Paper doi abstract bibtex
Paper doi abstract bibtex It has been shown that the template based approaches could quickly estimate 6D pose of texture-less objects from a monocular image. However, they tend to be slow when the number of templates amounts to tens of thousands for handling a wider range of 3D object pose. To alleviate this problem, we propose a novel image feature and a tree-structured model. Our proposed perspectively cumulated orientation feature (PCOF) is based on the orientation histograms extracted from randomly generated 2D projection images using 3D CAD data, and the template using PCOF explicitly handle a certain range of 3D object pose. The hierarchical pose trees (HPT) is built by clustering 3D object pose and reducing the resolutions of templates, and HPT accelerates 6D pose estimation based on a coarse-to-fine strategy with an image pyramid. In the experimental evaluation on our texture-less object dataset, the combination of PCOF and HPT showed higher accuracy and faster speed in comparison with state-of-the-art techniques.
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abstract = {It has been shown that the template based approaches could quickly estimate 6D pose of texture-less objects from a monocular image. However, they tend to be slow when the number of templates amounts to tens of thousands for handling a wider range of 3D object pose. To alleviate this problem, we propose a novel image feature and a tree-structured model. Our proposed perspectively cumulated orientation feature (PCOF) is based on the orientation histograms extracted from randomly generated 2D projection images using 3D CAD data, and the template using PCOF explicitly handle a certain range of 3D object pose. The hierarchical pose trees (HPT) is built by clustering 3D object pose and reducing the resolutions of templates, and HPT accelerates 6D pose estimation based on a coarse-to-fine strategy with an image pyramid. In the experimental evaluation on our texture-less object dataset, the combination of PCOF and HPT showed higher accuracy and faster speed in comparison with state-of-the-art techniques.},
bibtype = {article},
author = {Konishi, Y and Hanzawa, Y and Kawade, M and Hashimoto, M},
doi = {10.1007/978-3-319-46448-0},
journal = {Eccv}
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