Abstract. We propose a benchmark for 6D pose estimation of a rigid
object from a single RGB-D input image. The training data consists of
a texture-mapped 3D object model or images of the object in known 6D
poses. The benchmark comprises of: i) eight datasets in a unified format
that cover different practical scenarios, including two new datasets focusing on varying lighting conditions, ii) an evaluation methodology with a
pose-error function that deals with pose ambiguities, iii) a comprehensive
evaluation of 15 diverse recent methods that captures the status quo of
the field, and iv) an online evaluation system that is open for continuous submission of new results. The evaluation shows that methods based
on point-pair features currently perform best, outperforming template
matching methods, learning-based methods and methods based on 3D
local features. The project website is available at bop.felk.cvut.cz.