  This paper presents a stereo object matching method that exploits both 2D
contextual information from images as well as 3D object-level information.
Unlike existing stereo matching methods that exclusively focus on the
pixel-level correspondence between stereo images within a volumetric space
(i.e., cost volume), we exploit this volumetric structure in a different
manner. The cost volume explicitly encompasses 3D information along its
disparity axis, therefore it is a privileged structure that can encapsulate the
3D contextual information from objects. However, it is not straightforward
since the disparity values map the 3D metric space in a non-linear fashion.
Thus, we present two novel strategies to handle 3D objectness in the cost
volume space: selective sampling (RoISelect) and 2D-3D fusion
(fusion-by-occupancy), which allow us to seamlessly incorporate 3D object-level
information and achieve accurate depth performance near the object boundary
regions. Our depth estimation achieves competitive performance in the KITTI
dataset and the Virtual-KITTI 2.0 dataset.
