SparseFormer: Sparse transformer network for point cloud classification

Abstract

Compared to the traditional self-attention structure of Transformers, the MLP-like structure offers advantages such as simplicity and improved performance. However, effectively and efficiently learning features from sparse, irregular, and unordered 3D point cloud data remains a challenge. To address this issue, we propose SparseFormer, a sparse transformer network designed specifically for point cloud processing tasks. SparseFormer incorporates a sparse MLP module that enables accurate feature learning while considering the unique characteristics of 3D point cloud data. Additionally, we enhance the context information by utilizing a multi-scale feature aggregation module. Experimental results demonstrate the superior performance of SparseFormer on classification tasks using benchmark datasets, including the ModelNet40 synthetic dataset and the ScanObjectNN real-world dataset. In the classification experiment on the ScanObjectNN dataset, SparseFormer achieves a mean accuracy of 84.1% and an overall accuracy of 85.5%.