Point Cloud Classification Using Content-based Transformer via Clustering in Feature Space
This work improves point cloud classification for computer vision applications by enabling better long-range dependency capture with reduced computational complexity, though it is incremental as it builds on existing Transformer methods.
The paper tackles the problem of 3D point cloud classification by addressing the limitations of local spatial attention in Transformers, which ignore content and long-range dependencies, and proposes PointConT, a content-based Transformer that clusters points in feature space to compute self-attention within classes, achieving 90.3% Top-1 accuracy on the hardest setting of ScanObjectNN.
Recently, there have been some attempts of Transformer in 3D point cloud classification. In order to reduce computations, most existing methods focus on local spatial attention, but ignore their content and fail to establish relationships between distant but relevant points. To overcome the limitation of local spatial attention, we propose a point content-based Transformer architecture, called PointConT for short. It exploits the locality of points in the feature space (content-based), which clusters the sampled points with similar features into the same class and computes the self-attention within each class, thus enabling an effective trade-off between capturing long-range dependencies and computational complexity. We further introduce an Inception feature aggregator for point cloud classification, which uses parallel structures to aggregate high-frequency and low-frequency information in each branch separately. Extensive experiments show that our PointConT model achieves a remarkable performance on point cloud shape classification. Especially, our method exhibits 90.3% Top-1 accuracy on the hardest setting of ScanObjectNN. Source code of this paper is available at https://github.com/yahuiliu99/PointConT.