  3D point cloud segmentation is an important function that helps robots
understand the layout of their surrounding environment and perform tasks such
as grasping objects, avoiding obstacles, and finding landmarks. Current
segmentation methods are mostly class-specific, many of which are tuned to work
with specific object categories and may not be generalizable to different types
of scenes. This research proposes a learnable region growing method for
class-agnostic point cloud segmentation, specifically for the task of instance
label prediction. The proposed method is able to segment any class of objects
using a single deep neural network without any assumptions about their shapes
and sizes. The deep neural network is trained to predict how to add or remove
points from a point cloud region to morph it into incrementally more complete
regions of an object instance. Segmentation results on the S3DIS and ScanNet
datasets show that the proposed method outperforms competing methods by 1%-9%
on 6 different evaluation metrics.
