Automatic pseudo-labeling is a powerful tool to tap into large amounts of
sequential unlabeled data. It is specially appealing in safety-critical
applications of autonomous driving, where performance requirements are extreme,
datasets are large, and manual labeling is very challenging. We propose to
leverage sequences of point clouds to boost the pseudolabeling 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 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 and urban driving scenarios. We show the performance of our
method applied to 3D semantic segmentation and 3D object detection on three
benchmark datasets. Our approach, which uses only 20% manual labels,
outperforms some fully supervised methods. A notable performance boost is
achieved for classes rarely appearing in training data.Comment: This work has been submitted to the IEEE for publicatio