3D object detection has recently received much attention due to its great potential in autonomous vehicle (AV). The success of deep learning based object detectors relies on the availability of large-scale annotated datasets, which is time-consuming and expensive to compile, especially for 3D bounding box annotation. In this work, we investigate diversity-based active learning (AL) as a potential solution to alleviate the annotation burden. Given limited annotation budget, only the most informative frames and objects are automatically selected for human to annotate. Technically, we take the advantage of the multimodal information provided in an AV dataset, and propose a novel acquisition function that enforces spatial and temporal diversity in the selected samples. We benchmark the proposed method against other AL strategies under realistic annotation cost measurement, where the realistic costs for annotating a frame and a 3D bounding box are both taken into consideration. We demonstrate the effectiveness of the proposed method on the nuScenes dataset and show that it outperforms existing AL strategies significantly.
翻译:3D天体探测最近因其在自主载体(AV)中的巨大潜力而得到很大关注。深学习天体探测器的成功取决于是否有大型附加说明的数据集,这些数据集的编制耗费时间且费用昂贵,特别是用于3D边框注解。在这项工作中,我们调查基于多样性的积极学习(AL)作为减轻批注负担的一个潜在解决办法。鉴于注释预算有限,只有信息最丰富的框架和对象才能自动被选为人文注解。技术上,我们利用AV数据集提供的多式联运信息,并提议一种新的获取功能,在选定的样本中实施空间和时间多样性。我们根据现实的注解成本衡量法,将拟议方法与其他AL战略相比较,同时考虑说明框架和3D边框的实际成本。我们展示了在nuScenes数据集上拟议方法的有效性,并表明它大大超过现有的AL战略。