  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.
