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/.
翻译:3D物体探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就大量昂贵和耗时收集的附加说明的数据进行培训。 相反,我们提议通过通过时间图神经网络对3D物体探测器进行半监督的学习,从而利用大量未贴标签的点云视频。 我们的洞察力是,时间平滑可以对未贴标签的数据产生更准确的探测结果,然后这些平稳的探测可以用来对探测器进行再培训。 我们学会用图表神经网络来进行这种时间推理,其中边缘代表不同时间框架内候选人探测之间的关系。经过半监督的学习后,我们的方法在挑战性核传感器和H3D基准方面实现了最先进的探测性能,而与就同样数量标签数据培训的基线相比,项目和代码可以在https://www.jianrenw.com/SOD-TGNN/上发布。</s>