Local-Area-Learning Network: Meaningful Local Areas for Efficient Point Cloud Analysis

TL;DR

LocAL-Net introduces a learning-based method for selecting meaningful local areas in point clouds, enhancing local structure recognition and achieving superior classification performance over existing methods.

Contribution

The paper proposes a neural network that learns critical points as local area centers and assigns metric properties, improving local structure capture in point cloud analysis.

Findings

Outperforms state-of-the-art in point cloud classification.

Achieves competitive results in part segmentation.

Effectively captures local structures through learned local areas.

Abstract

Research in point cloud analysis with deep neural networks has made rapid progress in recent years. The pioneering work PointNet offered a direct analysis of point clouds. However, due to its architecture PointNet is not able to capture local structures. To overcome this drawback, the same authors have developed PointNet++ by applying PointNet to local areas. The local areas are defined by center points and their neighbors. In PointNet++ and its further developments the center points are determined with a Farthest Point Sampling (FPS) algorithm. This has the disadvantage that the center points in general do not have meaningful local areas. In this paper, we introduce the neural Local-Area-Learning Network (LocAL-Net) which places emphasis on the selection and characterization of the local areas. Our approach learns critical points that we use as center points. In order to strengthen the recognition of local structures, the points are given additional metric properties depending on the local areas. Finally, we derive and combine two global feature vectors, one from the whole point cloud and one from all local areas. Experiments on the datasets ModelNet10/40 and ShapeNet show that LocAL-Net is competitive for part segmentation. For classification LocAL-Net outperforms the state-of-the-arts.