  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.
