MLGPnet: Multi-granularity neural network for 3D shape recognition using pyramid data

Abstract

This paper presents a Multi-Granularity 3D shape recognition network comprising point-granularity, line-granularity, and Pyramid-granularity networks, as well as multi-granularity convolutional layers (MLGPnet). The network takes pyramid data with high-level features generated from mesh data as input. The point-granularity, line-granularity, and pyramid-granularity networks respectively generate features at the point, line, and pyramid levels. Finally, two multi-granularity convolutional layers merge the features from these different levels to generate more efficient 3D shape global features. Compared to some classical 3D shape recognition network models, the proposed network achieves superior results on three publicly general-purpose datasets. Notably, among all mesh-based recognition networks, the proposed network demonstrates the best recognition accuracy and retrieval rate. Furthermore, the proposed network model performs better in terms of training time and model complexity, with faster training time and fewer model parameters, resulting in faster recognition speed.