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:
this https URL

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。

面向无监督的基于 LiDAR 点云的物体检测

Towards Unsupervised Object Detection From LiDAR Point Clouds

We propose to harness the potential of simulation for the semantic
segmentation of real-world self-driving scenes in a domain generalization
fashion. The segmentation network is trained without any data of target domains
and tested on the unseen target domains. To this end, we propose a new approach
of domain randomization and pyramid consistency to learn a model with high
generalizability. First, we propose to randomize the synthetic images with the
styles of real images in terms of visual appearances using auxiliary datasets,
in order to effectively learn domain-invariant representations. Second, we
further enforce pyramid consistency across different "stylized" images and
within an image, in order to learn domain-invariant and scale-invariant
features, respectively. Extensive experiments are conducted on the
generalization from GTA and SYNTHIA to Cityscapes, BDDS and Mapillary; and our
method achieves superior results over the state-of-the-art techniques.
Remarkably, our generalization results are on par with or even better than
those obtained by state-of-the-art simulation-to-real domain adaptation
methods, which access the target domain data at training time.

本文提出一种使用模拟技术进行自动驾驶场景语义分割的方法，通过域泛化的方式进行测试并实现了高度通用性，其中使用了域随机化和金字塔一致性两种技术，实现了域不变的特征表示。经过在 GTA，SYNTHIA 到 Cityscapes，BDDS 和 Mapillary 的广泛实验，本方法在一些状态和最先进的仿真转现领域自适应方法的测试结果表明，本方法实现了有竞争力、甚至优于其他方法的结果。