  We present a novel technique to estimate the 6D pose of objects from single
images where the 3D geometry of the object is only given approximately and not
as a precise 3D model. To achieve this, we employ a dense 2D-to-3D
correspondence predictor that regresses 3D model coordinates for every pixel.
In addition to the 3D coordinates, our model also estimates the pixel-wise
coordinate error to discard correspondences that are likely wrong. This allows
us to generate multiple 6D pose hypotheses of the object, which we then refine
iteratively using a highly efficient region-based approach. We also introduce a
novel pixel-wise posterior formulation by which we can estimate the probability
for each hypothesis and select the most likely one. As we show in experiments,
our approach is capable of dealing with extreme visual conditions including
overexposure, high contrast, or low signal-to-noise ratio. This makes it a
powerful technique for the particularly challenging task of estimating the pose
of tumbling satellites for in-orbit robotic applications. Our method achieves
state-of-the-art performance on the SPEED+ dataset and has won the SPEC2021
post-mortem competition.
