论文标题
形状感知的单眼3D对象检测
Shape-Aware Monocular 3D Object Detection
论文作者
论文摘要
通过单个透视摄像机检测3D对象是一个具有挑战性的问题。由于其有效性和简单性，最近无锚的基于锚的模型最近受到了越来越多的关注。但是，这些方法中的大多数容易受到封闭和截断对象的影响。在本文中，提出了单阶段的单眼3D对象检测模型。实例细分头被集成到模型训练中，该训练使模型可以意识到目标对象的可见形状。该检测在很大程度上避免了目标物体周围无关区域的干扰。此外，我们还揭示了最初设计用于评估立体声或基于激光痛的检测方法的流行的评估指标对改善单眼3D对象检测算法不敏感。为单眼3D对象检测模型提出了一种新颖的评估度量，即平均深度相似性（AD）。我们的方法在维持实时效率的同时，在受欢迎的评估指标和拟议的评估指标上都优于基线。
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.
