OGC: Unsupervised 3D Object Segmentation from Rigid Dynamics of Point Clouds
This addresses the need for efficient 3D object segmentation without costly annotations, though it is incremental as it builds on existing unsupervised techniques.
The paper tackles the problem of 3D object segmentation from raw point clouds by proposing OGC, the first unsupervised method that identifies multiple objects without human annotations, achieving superior performance on five datasets in indoor and outdoor scenarios.
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.