  In this paper, we study the problem of 3D object detection from stereo
images, in which the key challenge is how to effectively utilize stereo
information. Different from previous methods using pixel-level depth maps, we
propose employing 3D anchors to explicitly construct object-level
correspondences between the regions of interest in stereo images, from which
the deep neural network learns to detect and triangulate the targeted object in
3D space. We also introduce a cost-efficient channel reweighting strategy that
enhances representational features and weakens noisy signals to facilitate the
learning process. All of these are flexibly integrated into a solid baseline
detector that uses monocular images. We demonstrate that both the monocular
baseline and the stereo triangulation learning network outperform the prior
state-of-the-arts in 3D object detection and localization on the challenging
KITTI dataset.
