The recent surge in 3D data acquisition has spurred the development of
geometric deep learning models for point cloud processing, boosted by the
remarkable success of transformers in natural language processing. While point
cloud transformers (PTs) have achieved impressive results recently, their
quadratic scaling with respect to the point cloud size poses a significant
scalability challenge for real-world applications. To address this issue, we
propose the Adaptive Point Cloud Transformer (AdaPT), a standard PT model
augmented by an adaptive token selection mechanism. AdaPT dynamically reduces
the number of tokens during inference, enabling efficient processing of large
point clouds. Furthermore, we introduce a budget mechanism to flexibly adjust
the computational cost of the model at inference time without the need for
retraining or fine-tuning separate models. Our extensive experimental
evaluation on point cloud classification tasks demonstrates that AdaPT
significantly reduces computational complexity while maintaining competitive
accuracy compared to standard PTs. The code for AdaPT is made publicly
available.

通过提出自适应点云变换器（AdaPT）模型，文章解决了点云变换器（PT）在处理大规模点云时的可扩展性挑战，实现了较低的计算复杂度和竞争性的分类准确率。