Dominated point cloud-based 3D object detectors in autonomous driving scenarios rely heavily on the huge amount of accurately labeled samples, however, 3D annotation in the point cloud is extremely tedious, expensive and time-consuming. To reduce the dependence on large supervision, semi-supervised learning (SSL) based approaches have been proposed. The Pseudo-Labeling methodology is commonly used for SSL frameworks, however, the low-quality predictions from the teacher model have seriously limited its performance. In this work, we propose a new Pseudo-Labeling framework for semi-supervised 3D object detection, by enhancing the teacher model to a proficient one with several necessary designs. First, to improve the recall of pseudo labels, a Spatialtemporal Ensemble (STE) module is proposed to generate sufficient seed boxes. Second, to improve the precision of recalled boxes, a Clusteringbased Box Voting (CBV) module is designed to get aggregated votes from the clustered seed boxes. This also eliminates the necessity of sophisticated thresholds to select pseudo labels. Furthermore, to reduce the negative influence of wrongly pseudo-labeled samples during the training, a soft supervision signal is proposed by considering Box-wise Contrastive Learning (BCL). The effectiveness of our model is verified on both ONCE and Waymo datasets. For example, on ONCE, our approach significantly improves the baseline by 9.51 mAP. Moreover, with half annotations, our model outperforms the oracle model with full annotations on Waymo.
翻译:在自主驾驶情况下,基于点云的三维天体探测器在很大程度上依赖大量贴有准确标签的样本,然而,在点云中,3D注解极为繁琐、昂贵和耗时。为减少对大型监督的依赖,提出了半监督学习(SSL)法。为改进对大型监督的依赖,提出了半监督学习(SSL)法,以产生足够的种子箱。第二,为了提高被召箱的精确度,教师模型的低质量预测严重限制了其性能。在这项工作中,我们提出了一个新的半监督的三维天体探测的Pseudo-Labe框架,通过将教师模型提升为精密的半监督型,将教师模型提升为精密的,同时进行若干必要设计。首先,为了改进假标签的回收,一个空间测试(SSLE)模块将生成足够的种子箱。第二,一个基于分组的箱投票模型旨在从集的种子箱中获取综合投票结果。这也消除了选择假标签的精密阈值的必要性。此外,为了降低伪标签的半半透明性说明的半透明性,在软性模型上,通过测试,一个软性模型将测试进行。