Towards Unsupervised Object Detection From LiDAR Point Clouds
Lunjun Zhang Anqi Joyce Yang Yuwen Xiong Sergio Casas Bin Yang Mengye Ren Raquel Urtasun
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