Shape-Aware Monocular 3D Object Detection

Abstract

The 3D object detection is the key issue in the autonomous driving system. This issue is particularly challenging when the detection only relies on a single perspective camera. 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 the occlusion and truncation of 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. Therefore, 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 achieved by the 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 comparison baseline in terms of both the popular and the proposed evaluation metrics while maintaining real-time efficiency.