  Accurate 3D object detection in LiDAR point clouds is crucial for autonomous
driving systems. To achieve state-of-the-art performance, the supervised
training of detectors requires large amounts of human-annotated data, which is
expensive to obtain and restricted to predefined object categories. To mitigate
manual labeling efforts, recent unsupervised object detection approaches
generate class-agnostic pseudo-labels for moving objects, subsequently serving
as supervision signal to bootstrap a detector. Despite promising results, these
approaches do not provide class labels or generalize well to static objects.
Furthermore, they are mostly restricted to data containing multiple drives from
the same scene or images from a precisely calibrated and synchronized camera
setup. To overcome these limitations, we propose a vision-language-guided
unsupervised 3D detection approach that operates exclusively on LiDAR point
clouds. We transfer CLIP knowledge to classify point clusters of static and
moving objects, which we discover by exploiting the inherent spatio-temporal
information of LiDAR point clouds for clustering, tracking, as well as box and
label refinement. Our approach outperforms state-of-the-art unsupervised 3D
object detectors on the Waymo Open Dataset ($+23~\text{AP}_{3D}$) and Argoverse
2 ($+7.9~\text{AP}_{3D}$) and provides class labels not solely based on object
size assumptions, marking a significant advancement in the field.
