  This paper presents the evaluation methodology, datasets, and results of the
BOP Challenge 2020, the third in a series of public competitions organized with
the goal to capture the status quo in the field of 6D object pose estimation
from an RGB-D image. In 2020, to reduce the domain gap between synthetic
training and real test RGB images, the participants were provided 350K
photorealistic training images generated by BlenderProc4BOP, a new open-source
and light-weight physically-based renderer (PBR) and procedural data generator.
Methods based on deep neural networks have finally caught up with methods based
on point pair features, which were dominating previous editions of the
challenge. Although the top-performing methods rely on RGB-D image channels,
strong results were achieved when only RGB channels were used at both training
and test time - out of the 26 evaluated methods, the third method was trained
on RGB channels of PBR and real images, while the fifth on RGB channels of PBR
images only. Strong data augmentation was identified as a key component of the
top-performing CosyPose method, and the photorealism of PBR images was
demonstrated effective despite the augmentation. The online evaluation system
stays open and is available on the project website: bop.felk.cvut.cz.
