  3D object detection from a single image is an important task in Autonomous
Driving (AD), where various approaches have been proposed. However, the task is
intrinsically ambiguous and challenging as single image depth estimation is
already an ill-posed problem. In this paper, we propose an instance-aware
approach to aggregate useful information for improving the accuracy of 3D
object detection with the following contributions. First, an instance-aware
feature aggregation (IAFA) module is proposed to collect local and global
features for 3D bounding boxes regression. Second, we empirically find that the
spatial attention module can be well learned by taking coarse-level instance
annotations as a supervision signal. The proposed module has significantly
boosted the performance of the baseline method on both 3D detection and 2D
bird-eye's view of vehicle detection among all three categories. Third, our
proposed method outperforms all single image-based approaches (even these
methods trained with depth as auxiliary inputs) and achieves state-of-the-art
3D detection performance on the KITTI benchmark.
