  The detection of 3D objects through a single perspective camera is a
challenging issue. The anchor-free and keypoint-based models receive increasing
attention recently due to their effectiveness and simplicity. However, most of
these methods are vulnerable to occluded and truncated objects. In this paper,
a single-stage monocular 3D object detection model is proposed. An
instance-segmentation head is integrated into the model training, which allows
the model to be aware of the visible shape of a target object. The detection
largely avoids interference from irrelevant regions surrounding the target
objects. In addition, we also reveal that the popular IoU-based evaluation
metrics, which were originally designed for evaluating stereo or LiDAR-based
detection methods, are insensitive to the improvement of monocular 3D object
detection algorithms. A novel evaluation metric, namely average depth
similarity (ADS) is proposed for the monocular 3D object detection models. Our
method outperforms the baseline on both the popular and the proposed evaluation
metrics while maintaining real-time efficiency.
