EPOS: Estimating 6D Pose of Objects with Symmetries

• URL: http://arxiv.org/abs/2004.00605v1
• Date: Wed, 1 Apr 2020 17:41:08 GMT
• Title: EPOS: Estimating 6D Pose of Objects with Symmetries
• Authors: Tomas Hodan, Daniel Barath, Jiri Matas
• Abstract summary: We present a new method for estimating the 6D pose of rigid objects with available 3D models from a single RGB input. An object is represented by compact surface fragments which allow symmetries in a systematic manner. Correspondences between densely sampled pixels and the fragments are predicted using an encoder-decoder network.
• Score: 57.448933686429825
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present a new method for estimating the 6D pose of rigid objects with available 3D models from a single RGB input image. The method is applicable to a broad range of objects, including challenging ones with global or partial symmetries. An object is represented by compact surface fragments which allow handling symmetries in a systematic manner. Correspondences between densely sampled pixels and the fragments are predicted using an encoder-decoder network. At each pixel, the network predicts: (i) the probability of each object's presence, (ii) the probability of the fragments given the object's presence, and (iii) the precise 3D location on each fragment. A data-dependent number of corresponding 3D locations is selected per pixel, and poses of possibly multiple object instances are estimated using a robust and efficient variant of the PnP-RANSAC algorithm. In the BOP Challenge 2019, the method outperforms all RGB and most RGB-D and D methods on the T-LESS and LM-O datasets. On the YCB-V dataset, it is superior to all competitors, with a large margin over the second-best RGB method. Source code is at: cmp.felk.cvut.cz/epos.

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