Point-GR: Graph Residual Point Cloud Network for 3D Object Classification and Segmentation

TL;DR

Point-GR is a novel deep learning architecture for 3D point cloud analysis that effectively captures local geometric features and reduces model complexity, achieving state-of-the-art segmentation results.

Contribution

The paper introduces Point-GR, a residual-based graph neural network that enhances 3D point cloud processing with fewer parameters and improved segmentation accuracy.

Findings

Achieves 73.47% mean IoU on S3DIS segmentation benchmark

Reduces network parameters compared to baseline graph networks

Demonstrates competitive performance in classification and segmentation

Abstract

In recent years, the challenge of 3D shape analysis within point cloud data has gathered significant attention in computer vision. Addressing the complexities of effective 3D information representation and meaningful feature extraction for classification tasks remains crucial. This paper presents Point-GR, a novel deep learning architecture designed explicitly to transform unordered raw point clouds into higher dimensions while preserving local geometric features. It introduces residual-based learning within the network to mitigate the point permutation issues in point cloud data. The proposed Point-GR network significantly reduced the number of network parameters in Classification and Part-Segmentation compared to baseline graph-based networks. Notably, the Point-GR model achieves a state-of-the-art scene segmentation mean IoU of 73.47% on the S3DIS benchmark dataset, showcasing its effectiveness. Furthermore, the model shows competitive results in Classification and Part-Segmentation tasks.