  State-of-the-art object pose estimation handles multiple instances in a test
image by using multi-model formulations: detection as a first stage and then
separately trained networks per object for 2D-3D geometric correspondence
prediction as a second stage. Poses are subsequently estimated using the
Perspective-n-Points algorithm at runtime. Unfortunately, multi-model
formulations are slow and do not scale well with the number of object instances
involved. Recent approaches show that direct 6D object pose estimation is
feasible when derived from the aforementioned geometric correspondences. We
present an approach that learns an intermediate geometric representation of
multiple objects to directly regress 6D poses of all instances in a test image.
The inherent end-to-end trainability overcomes the requirement of separately
processing individual object instances. By calculating the mutual
Intersection-over-Unions, pose hypotheses are clustered into distinct
instances, which achieves negligible runtime overhead with respect to the
number of object instances. Results on multiple challenging standard datasets
show that the pose estimation performance is superior to single-model
state-of-the-art approaches despite being more than ~35 times faster. We
additionally provide an analysis showing real-time applicability (>24 fps) for
images where more than 90 object instances are present. Further results show
the advantage of supervising geometric-correspondence-based object pose
estimation with the 6D pose.
