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      Deep Learning of Local RGB-D Patches for 3D Object Detection and 6D Pose Estimation

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          Abstract

          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 with and surpass the state-of-the-art while being scalable in the number of objects.

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          Author and article information

          Journal
          2016-07-20
          Article
          1607.06038
          926bea54-8a0f-4c17-9db6-13dd798f6fcc

          http://arxiv.org/licenses/nonexclusive-distrib/1.0/

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          Custom metadata
          To appear at ECCV 2016
          cs.CV

          Computer vision & Pattern recognition
          Computer vision & Pattern recognition

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