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:
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我们研究在自动驾驶场景中从 3D 点云中进行无监督对象检测的问题，提出了一种简单而有效的方法，利用点聚类、时间一致性、CNN 的平移等性质以及自我监督来改进无监督检测算法，通过在非相近区域进行自动标记扩展到远距离，从而实现了零 - shot 方式的对象检测，能够在稀疏、远距离区域进行检测，同时在更多迭代自我训练的过程中不断改进。我们提出了基于碰撞距离的新的规划感知度量来更好地衡量模型在自动驾驶场景中的性能。我们在 PandaSet 和 Argoverse 2 Sensor 数据集上展示了我们的无监督对象检测器显著优于无监督基线的性能，证明了自我监督与对象先验相结合可以实现野外对象发现的潜力。