  Estimating the 6D pose of objects accurately, quickly, and robustly remains a
difficult task. However, recent methods for directly regressing poses from RGB
images using dense features have achieved state-of-the-art results. Stereo
vision, which provides an additional perspective on the object, can help reduce
pose ambiguity and occlusion. Moreover, stereo can directly infer the distance
of an object, while mono-vision requires internalized knowledge of the object's
size. To extend the state-of-the-art in 6D object pose estimation to stereo, we
created a BOP compatible stereo version of the YCB-V dataset. Our method
outperforms state-of-the-art 6D pose estimation algorithms by utilizing stereo
vision and can easily be adopted for other dense feature-based algorithms.
