论文标题
通过时间图神经网络进行半监督的3D对象检测
Semi-supervised 3D Object Detection via Temporal Graph Neural Networks
论文作者
论文摘要
3D对象检测在自主驾驶和其他机器人应用中起着重要作用。但是，这些探测器通常需要对大量注释的数据进行培训，这些数据昂贵且耗时。取而代之的是，我们建议通过时间图神经网络对3D对象检测器的半监督学习来利用大量未标记的点云视频。我们的见解是，时间平滑可以在未标记的数据上创建更准确的检测结果，然后可以使用这些平滑检测来重新训练检测器。我们学会使用图形神经网络执行此时间推理，其中边缘代表不同时间范围中候选检测之间的关系。经过半监督的学习，与接受相同数量的标记数据训练的基线相比，我们的方法在具有挑战性的Nuscenes和H3D基准上实现了最先进的检测性能。项目和代码在https://www.jianrenw.com/sod-tgnn/上发布。
3D object detection plays an important role in autonomous driving and other robotics applications. However, these detectors usually require training on large amounts of annotated data that is expensive and time-consuming to collect. Instead, we propose leveraging large amounts of unlabeled point cloud videos by semi-supervised learning of 3D object detectors via temporal graph neural networks. Our insight is that temporal smoothing can create more accurate detection results on unlabeled data, and these smoothed detections can then be used to retrain the detector. We learn to perform this temporal reasoning with a graph neural network, where edges represent the relationship between candidate detections in different time frames. After semi-supervised learning, our method achieves state-of-the-art detection performance on the challenging nuScenes and H3D benchmarks, compared to baselines trained on the same amount of labeled data. Project and code are released at https://www.jianrenw.com/SOD-TGNN/.
