Efficient point cloud segmentation approach using energy optimization with geometric features for 3D scene understanding.

Efficient and quick extraction of unknown objects in cluttered 3D scenes plays a significant role in robotics tasks such as object search, grasping, and manipulation. This paper describes a geometric-based unsupervised approach for the segmentation of cluttered scenes into objects. The proposed method first over-segments the raw point clouds into supervoxels to provide a more natural representation of 3D point clouds and reduce the computational cost with a minimal loss of geometric information. Then the fully connected local area linkage graph is used to distinguish between planar and nonplanar adjacent patches. Then the initial segmentation is completed utilizing the geometric features and local surface convexities. After the initial segmentation, many subgraphs are generated, each of which represents an individual object or part of it. Finally, we use the plane extracted from the scene to refine the initial segmentation result under the framework of global energy optimization. Experiments on the Object Cluttered Indoor Dataset dataset indicate that the proposed method can outperform the representative segmentation algorithms in terms of weighted overlap and accuracy, while our method has good robustness and real-time performance.