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- W32934609 abstract "Category-level object detection, the task of locating object instances of a given category in images, has been tackled with many al- gorithms employing standard color images. Less attention has been given to solving it using range and depth data, which has lately become read- ily available using laser and RGB-D cameras. Exploiting the different nature of the depth modality, we propose a novel shape-based object detector with partial pose estimation for axial or reflection symmetric objects. We estimate this partial pose by detecting target's symmetry, which as a global mid-level feature provides us with a robust frame of reference with which shape features are represented for detection. Re- sults are shown on a particularly challenging depth dataset and exhibit significant improvement compared to the prior art." @default.
- W32934609 created "2016-06-24" @default.
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- W32934609 date "2014-01-01" @default.
- W32934609 modified "2023-09-27" @default.
- W32934609 title "Depth Based Object Detection from Partial Pose Estimation of Symmetric Objects" @default.
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- W32934609 doi "https://doi.org/10.1007/978-3-319-10602-1_25" @default.
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