Teachers in concordance for pseudo-labeling of 3D sequential data

Automatic pseudo-labeling is a powerful tool to tap into large amounts of sequential unlabeled data. It is especially appealing in safety-critical applications of autonomous driving where performance requirements are extreme, datasets large, and manual labeling is very challenging. We propose to leverage the sequentiality of the captures to boost the pseudo-labeling technique in a teacher-student setup via training multiple teachers, each with access to different temporal information. This set of teachers, dubbed Concordance, provides higher quality pseudo-labels for the student training than standard methods. The output of multiple teachers is combined via a novel pseudo-label confidence-guided criterion. Our experimental evaluation focuses on the 3D point cloud domain in urban driving scenarios. We show the performance of our method applied to multiple model architectures with tasks of 3D semantic segmentation and 3D object detection on two benchmark datasets. Our method, using only 20% of manual labels, outperforms some of the fully supervised methods. Special performance boost is achieved for classes rarely appearing in the training data, e.g., bicycles and pedestrians. The implementation of our approach is publicly available at https://github.com/ctu-vras/T-Concord3D.