论文标题
在自动驾驶中探索基于多样性的主动学习以进行3D对象检测
Exploring Diversity-based Active Learning for 3D Object Detection in Autonomous Driving
论文作者
论文摘要
由于其在自动驾驶汽车（AV）中的巨大潜力，3D对象检测最近受到了很多关注。基于深度学习的对象检测器的成功取决于大规模注释的数据集的可用性，该数据集耗时且编译昂贵，尤其是对于3D边界框注释。在这项工作中，我们研究了基于多样性的积极学习（AL），以减轻注释负担的潜在解决方案。鉴于有限的注释预算，只有最有用的框架和对象自动选择人类注释。从技术上讲，我们利用AV数据集中提供的多模式信息的优势，并提出了一种新颖的采集函数，可以在所选样品中实现空间和时间多样性。我们在现实的注释成本衡量下对其他AL策略进行基准对其他AL策略进行了基准测试，在该方法中，框架和3D边界框的现实成本都被考虑。我们在Nuscenes数据集上证明了拟议方法的有效性，并表明它的表现明显优于现有策略。代码可从https://github.com/linkon87/lexploring-diversity-基于-Active-active-active-learning-for-3d-object-detection-in-autonicous-drive
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. Code is available at https://github.com/Linkon87/Exploring-Diversity-based-Active-Learning-for-3D-Object-Detection-in-Autonomous-Driving
