Chair for Computer Aided Medical Procedures & Augmented Reality
Lehrstuhl für Informatikanwendungen in der Medizin & Augmented Reality

W. Kehl, F. Milletari, F. Tombari, S. Ilic, N. Navab
Deep Learning of Local RGB-D Patches for 3D Object Detection and 6D Pose Estimation
European Conference On Computer Vision (ECCV), Amsterdam, The Netherlands, October 2016 (bib)

We present a 3D object detection method that uses regressed descriptors of locally-sampled RGB-D patches for 6D vote casting. For regression, we employ a convolutional auto-encoder that has been trained on a large collection of random local patches. During testing, scene patch descriptors are matched against a database of synthetic model view patches and cast 6D object votes which are subsequently filtered to refined hypotheses. We evaluate on three datasets to show that our method generalizes well to previously unseen input data, delivers robust detection results that compete and surpass the state-of-the-art while being scalable in the number of objects.
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Edit | Attach | Refresh | Diffs | More | Revision r1.11 - 19 Jul 2016 - 16:26 - NassirNavab

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