Unsupervised 3D object detection methods have emerged to leverage vast
amounts of data efficiently without requiring manual labels for training.
Recent approaches rely on dynamic objects for learning to detect objects but
penalize the detections of static instances during training. Multiple rounds of
(self) training are used in which detected static instances are added to the
set of training targets; this procedure to improve performance is
computationally expensive. To address this, we propose the method UNION. We use
spatial clustering and self-supervised scene flow to obtain a set of static and
dynamic object proposals from LiDAR. Subsequently, object proposals' visual
appearances are encoded to distinguish static objects in the foreground and
background by selecting static instances that are visually similar to dynamic
objects. As a result, static and dynamic foreground objects are obtained
together, and existing detectors can be trained with a single training. In
addition, we extend 3D object discovery to detection by using object
appearance-based cluster labels as pseudo-class labels for training object
classification. We conduct extensive experiments on the nuScenes dataset and
increase the state-of-the-art performance for unsupervised object discovery,
i.e. UNION more than doubles the average precision to 33.9. The code will be
made publicly available.

通过使用无监督的三维物体检测方法，利用大量数据而不需要手动标注进行训练，我们提出了一种名为 UNION 的方法，通过空间聚类和自监督场景流来获取来自 LiDAR 的静态和动态物体提案，并通过编码物体提案的视觉外观来区分前景和背景中的静态物体，从而实现联合训练现有检测器的目的。此外，我们使用基于物体外观的聚类标签作为训练物体分类的伪类标签，将三维物体发现扩展到检测任务中。经过在 nuScenes 数据集上进行广泛实验，我们将无监督物体发现的最优性能提高到 33.9，超过当前技术水平的两倍。代码将公开发布。