AIFormer: Adaptive Interaction Transformer for 3D Point Cloud Understanding

Abstract

Recently, significant advancements have been made in 3D point cloud analysis by leveraging transformer architecture in 3D space. However, it remains challenging to effectively implement local and global learning within irregular and sparse structures of 3D point clouds. This paper presents the Adaptive Interaction Transformer (AIFormer), a novel hierarchical transformer architecture designed to enhance 3D point cloud analysis by fusing local and global features through the adaptive interaction of features. Specifically, AIFormer mainly consists of several stacked AIFormer Blocks. Each AIFormer module employs the Local Relation Aggregation Module and the Global Context Aggregation Module, respectively, to extract local details of relationships within the reference point and long-range dependencies between reference points. Then, the local and global features are fused using the Adaptive Interaction Module for adaptive interaction to optimize the point representation. Additionally, the AIFormer Block further designs geometric relation functions and contextual relative semantic encoding to enhance local and global feature extraction capabilities, respectively. Extensive experiments on three popular 3D point cloud datasets verify that AIFormer achieves state-of-the-art or comparable performances. Our comprehensive ablation study further validates the effectiveness and soundness of the AIFormer design.