  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.
