  In this paper, we study the problem of 3D object segmentation from raw point
clouds. Unlike all existing methods which usually require a large amount of
human annotations for full supervision, we propose the first unsupervised
method, called OGC, to simultaneously identify multiple 3D objects in a single
forward pass, without needing any type of human annotations. The key to our
approach is to fully leverage the dynamic motion patterns over sequential point
clouds as supervision signals to automatically discover rigid objects. Our
method consists of three major components, 1) the object segmentation network
to directly estimate multi-object masks from a single point cloud frame, 2) the
auxiliary self-supervised scene flow estimator, and 3) our core object geometry
consistency component. By carefully designing a series of loss functions, we
effectively take into account the multi-object rigid consistency and the object
shape invariance in both temporal and spatial scales. This allows our method to
truly discover the object geometry even in the absence of annotations. We
extensively evaluate our method on five datasets, demonstrating the superior
performance for object part instance segmentation and general object
segmentation in both indoor and the challenging outdoor scenarios.
