论文标题
回到现实：使用形状引导的标签增强功能弱监督的3D对象检测
Back to Reality: Weakly-supervised 3D Object Detection with Shape-guided Label Enhancement
论文作者
论文摘要
在本文中，我们提出了一种弱监督的3D对象检测方法，这使得可以使用位置级注释（即对象中心的注释）训练强3D检测器。为了纠正从框注释到中心的信息损失，我们的方法（即返回现实（BR））使用合成3D形状将弱标签转换为完全注销的虚拟场景，以更强大的监督，然后将完美的虚拟标签与真实的标签进行补充和完美。具体而言，我们首先根据从位置级别的注释中提取的粗场布局将3D形状组装到物理合理的虚拟场景中。然后，我们通过应用虚拟到现实的域适应方法回到现实，该方法可以完善弱标签，并通过虚拟场景来监督检测器的训练。此外，我们为室内3D对象检测提出了一个更具挑战性的Benckmark，其物体大小的多样性更加多样性，以更好地显示BR的潜力。在不到5％的标签劳动力范围内，我们在广泛使用的扫描仪数据集中采用了一些流行的全面监督方法，实现了可比的检测性能。代码可在以下网址找到：https：//github.com/wyf-accept/backtoreality
In this paper, we propose a weakly-supervised approach for 3D object detection, which makes it possible to train a strong 3D detector with position-level annotations (i.e. annotations of object centers). In order to remedy the information loss from box annotations to centers, our method, namely Back to Reality (BR), makes use of synthetic 3D shapes to convert the weak labels into fully-annotated virtual scenes as stronger supervision, and in turn utilizes the perfect virtual labels to complement and refine the real labels. Specifically, we first assemble 3D shapes into physically reasonable virtual scenes according to the coarse scene layout extracted from position-level annotations. Then we go back to reality by applying a virtual-to-real domain adaptation method, which refine the weak labels and additionally supervise the training of detector with the virtual scenes. Furthermore, we propose a more challenging benckmark for indoor 3D object detection with more diversity in object sizes to better show the potential of BR. With less than 5% of the labeling labor, we achieve comparable detection performance with some popular fully-supervised approaches on the widely used ScanNet dataset. Code is available at: https://github.com/wyf-ACCEPT/BackToReality
