  We focus on the challenging problem of efficient mouse 3D pose estimation
based on static images, and especially single depth images. We introduce an
approach to discriminatively train the split nodes of trees in random forest to
improve their performance on estimation of 3D joint positions of mouse. Our
algorithm is capable of working with different types of rodents and with
different types of depth cameras and imaging setups. In particular, it is
demonstrated in this paper that when a top-mounted depth camera is combined
with a bottom-mounted color camera, the final system is capable of delivering
full-body pose estimation including four limbs and the paws. Empirical
examinations on synthesized and real-world depth images confirm the
applicability of our approach on mouse pose estimation, as well as the closely
related task of part-based labeling of mouse.
