  Deep learning has led to great progress in the detection of mobile (i.e.
movement-capable) objects in urban driving scenes in recent years. Supervised
approaches typically require the annotation of large training sets; there has
thus been great interest in leveraging weakly, semi- or self-supervised methods
to avoid this, with much success. Whilst weakly and semi-supervised methods
require some annotation, self-supervised methods have used cues such as motion
to relieve the need for annotation altogether. However, a complete absence of
annotation typically degrades their performance, and ambiguities that arise
during motion grouping can inhibit their ability to find accurate object
boundaries. In this paper, we propose a new self-supervised mobile object
detection approach called SCT. This uses both motion cues and expected object
sizes to improve detection performance, and predicts a dense grid of 3D
oriented bounding boxes to improve object discovery. We significantly
outperform the state-of-the-art self-supervised mobile object detection method
TCR on the KITTI tracking benchmark, and achieve performance that is within 30%
of the fully supervised PV-RCNN++ method for IoUs <= 0.5.
