MG-SAGC: A multiscale graph and its self-adaptive graph convolution network for 3D point clouds

TL;DR

This paper introduces a multiscale graph generation and self-adaptive convolution approach for 3D point clouds, improving feature extraction and outperforming existing models on public datasets.

Contribution

It proposes a novel multiscale graph generation method and a self-adaptive graph convolution kernel based on Chebyshev polynomials for 3D point cloud analysis.

Findings

Significantly outperforms state-of-the-art models on public datasets

Enhances local feature extraction in point clouds

Demonstrates strong generalizability

Abstract

To enhance the ability of neural networks to extract local point cloud features and improve their quality, in this paper, we propose a multiscale graph generation method and a self-adaptive graph convolution method. First, we propose a multiscale graph generation method for point clouds. This approach transforms point clouds into a structured multiscale graph form that supports multiscale analysis of point clouds in the scale space and can obtain the dimensional features of point cloud data at different scales, thus making it easier to obtain the best point cloud features. Because traditional convolutional neural networks are not applicable to graph data with irregular vertex neighborhoods, this paper presents an sef-adaptive graph convolution kernel that uses the Chebyshev polynomial to fit an irregular convolution filter based on the theory of optimal approximation. In this paper, we adopt max pooling to synthesize the features of different scale maps and generate the point cloud features. In experiments conducted on three widely used public datasets, the proposed method significantly outperforms other state-of-the-art models, demonstrating its effectiveness and generalizability.