  We present the evaluation methodology, datasets and results of the BOP
Challenge 2022, the fourth 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/RGB-D image. In 2022, we witnessed another significant improvement
in the pose estimation accuracy -- the state of the art, which was 56.9 AR$_C$
in 2019 (Vidal et al.) and 69.8 AR$_C$ in 2020 (CosyPose), moved to new heights
of 83.7 AR$_C$ (GDRNPP). Out of 49 pose estimation methods evaluated since
2019, the top 18 are from 2022. Methods based on point pair features, which
were introduced in 2010 and achieved competitive results even in 2020, are now
clearly outperformed by deep learning methods. The synthetic-to-real domain gap
was again significantly reduced, with 82.7 AR$_C$ achieved by GDRNPP trained
only on synthetic images from BlenderProc. The fastest variant of GDRNPP
reached 80.5 AR$_C$ with an average time per image of 0.23s. Since most of the
recent methods for 6D object pose estimation begin by detecting/segmenting
objects, we also started evaluating 2D object detection and segmentation
performance based on the COCO metrics. Compared to the Mask R-CNN results from
CosyPose in 2020, detection improved from 60.3 to 77.3 AP$_C$ and segmentation
from 40.5 to 58.7 AP$_C$. The online evaluation system stays open and is
available at: \href{http://bop.felk.cvut.cz/}{bop.felk.cvut.cz}.
