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Depth Based Object Detection from Partial Pose Estimation of Symmetric Objects
Ehud Barnea and Ohad Ben-Shahar
Dept. of Computer Science, Ben-Gurion University of the Negev, Beer Sheva, Israelbarneaeh@cs.bgu.ac.il
ben-shahar@cs.bgu.ac.il
Abstract. Category-level object detection, the task of locating object instances of a given category in images, has been tackled with many algorithms employing standard color images. Less attention has been given to solving it using range and depth data, which has lately become readily 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. Results are shown on a particularly challenging depth dataset and exhibit significant improvement compared to the prior art.
Keywords: Object detection, 3D computer vision, Range data, Partial pose estimation
LNCS 8693, p. 377 ff.
lncs@springer.com
© Springer International Publishing Switzerland 2014