We propose an approach for view-invariant object detection directly in 3D with following properties: (i) The detection is based on matching of 3D contours to 3D object models. (ii) The matching is constrained with qualitative spatial relations such as above/below, left/right, and front/back. (iii) In order to ensure that any matching solution satisfies these constraints, we formulate the matching problem as finding maximum weight subgraphs with hard constraints, and utilize a novel inference framework to solve this problem. Given a single view of an RGB-D camera, we obtain 3D contours by "back projecting" 2D contours extracted in the depth map. As our experimental results demonstrate, the proposed approach significantly outperforms the state-of-the-art 2D approaches, in particular, latent SVM object detector, as well as recently proposed approaches for object detection in RGB-D data. © 2013 Springer-Verlag.
CITATION STYLE
Ma, T., Yi, M., & Latecki, L. J. (2013). View-invariant object detection by matching 3D contours. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7729 LNCS, pp. 183–196). https://doi.org/10.1007/978-3-642-37484-5_16
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