  3D detection plays an indispensable role in environment perception. Due to
the high cost of commonly used LiDAR sensor, stereo vision based 3D detection,
as an economical yet effective setting, attracts more attention recently. For
these approaches based on 2D images, accurate depth information is the key to
achieve 3D detection, and most existing methods resort to a preliminary stage
for depth estimation. They mainly focus on the global depth and neglect the
property of depth information in this specific task, namely, sparsity and
locality, where exactly accurate depth is only needed for these 3D bounding
boxes. Motivated by this finding, we propose a stereo-image based anchor-free
3D detection method, called structure-aware stereo 3D detector (termed as
SIDE), where we explore the instance-level depth information via constructing
the cost volume from RoIs of each object. Due to the information sparsity of
local cost volume, we further introduce match reweighting and structure-aware
attention, to make the depth information more concentrated. Experiments
conducted on the KITTI dataset show that our method achieves the
state-of-the-art performance compared to existing methods without depth map
supervision.
