  3D object detection is an essential task in autonomous driving and robotics.
Though great progress has been made, challenges remain in estimating 3D pose
for distant and occluded objects. In this paper, we present a novel framework
named ZoomNet for stereo imagery-based 3D detection. The pipeline of ZoomNet
begins with an ordinary 2D object detection model which is used to obtain pairs
of left-right bounding boxes. To further exploit the abundant texture cues in
RGB images for more accurate disparity estimation, we introduce a conceptually
straight-forward module -- adaptive zooming, which simultaneously resizes 2D
instance bounding boxes to a unified resolution and adjusts the camera
intrinsic parameters accordingly. In this way, we are able to estimate
higher-quality disparity maps from the resized box images then construct dense
point clouds for both nearby and distant objects. Moreover, we introduce to
learn part locations as complementary features to improve the resistance
against occlusion and put forward the 3D fitting score to better estimate the
3D detection quality. Extensive experiments on the popular KITTI 3D detection
dataset indicate ZoomNet surpasses all previous state-of-the-art methods by
large margins (improved by 9.4% on APbv (IoU=0.7) over pseudo-LiDAR). Ablation
study also demonstrates that our adaptive zooming strategy brings an
improvement of over 10% on AP3d (IoU=0.7). In addition, since the official
KITTI benchmark lacks fine-grained annotations like pixel-wise part locations,
we also present our KFG dataset by augmenting KITTI with detailed instance-wise
annotations including pixel-wise part location, pixel-wise disparity, etc..
Both the KFG dataset and our codes will be publicly available at
https://github.com/detectRecog/ZoomNet.
