DGC-TnT: Enhancing Point Cloud Object Classification by Dynamic Graph Convolutions With Transformer in Transformer

Abstract

Recently, deep learning neural networks have been widely used in object classification. The process of object classification typically involves extracting features from the point cloud using neural networks and integrating these features into a global feature for recognizing the object. However, most existing methods perform well in global feature extraction but still fall short in capturing local details, or lack an attention mechanism to focus on informative feature signals. This paper presents DGC-TnT which combines dynamic graph convolution (DGC) structure with the Point-TnT module. Employing a two-stage TnT architecture, DGC-TnT utilizes a self-attention mechanism to deeply analyze local features derived from dynamic graph convolution, expanding their scope globally. This mechanism effectively operates between individual points and clusters of points, thereby enhancing overall object classification performance. DGC-TnT facilitates enhanced feature communication between points and point clusters within the point cloud through the Transformer module. On the ModelNet40 and ScanobjectNN datasets, DGC-TnT has a significant improvement in accuracy.

Keywords

• Object classification
• dynamic graph convolution (DGC)
• global feature
• local feature
• transformer in transformer (TnT)
• edge-convolution (EdgeConv)