  In this paper, we study the problem of unsupervised object detection from 3D
point clouds in self-driving scenes. We present a simple yet effective method
that exploits (i) point clustering in near-range areas where the point clouds
are dense, (ii) temporal consistency to filter out noisy unsupervised
detections, (iii) translation equivariance of CNNs to extend the auto-labels to
long range, and (iv) self-supervision for improving on its own. Our approach,
OYSTER (Object Discovery via Spatio-Temporal Refinement), does not impose
constraints on data collection (such as repeated traversals of the same
location), is able to detect objects in a zero-shot manner without supervised
finetuning (even in sparse, distant regions), and continues to self-improve
given more rounds of iterative self-training. To better measure model
performance in self-driving scenarios, we propose a new planning-centric
perception metric based on distance-to-collision. We demonstrate that our
unsupervised object detector significantly outperforms unsupervised baselines
on PandaSet and Argoverse 2 Sensor dataset, showing promise that
self-supervision combined with object priors can enable object discovery in the
wild. For more information, visit the project website:
https://waabi.ai/research/oyster
