Unsupervised distribution-aware keypoints generation from 3D point clouds.

Keypoints extraction from 3D objects is a fundamental task in point cloud processing. The ideal keypoints should be an ordered and well-aligned set of points that effectively reflect the shape and structure of the object. To this end, this paper proposes an unsupervised 3D point cloud keypoints generation network with the consideration of the probability distribution of keypoints and spatial distribution among keypoints. The network downsamples and groups the 3D point cloud, obtaining local features of the point cloud. The local features are leveraged to explicitly learn the mixture probability distribution of keypoint position. A composite loss function that comprehensively considers shape similarity, point importance, and geometric constraint is proposed to guide the network in generating keypoints with semantic consistency and regular spatial distribution. The experimental results and quantitative comparisons on the ShapeNet and KeypointNet datasets demonstrate that the proposed method achieves ordered, well-aligned, and robust keypoints generation for 3D point clouds. The source code of the proposed method is available at https://github.com/djzgroup/Keypoints.