Additionally, the authors introduce a budget mechanism that can adjust the computational cost of the model at inference time without requiring retraining or fine-tuning of separate models. Experimental evaluation on point cloud classification tasks shows that AdaPT significantly reduces computational complexity while maintaining competitive accuracy compared to standard PTs. The code for AdaPT is publicly available.
Key takeaways:
- The paper introduces the Adaptive Point Cloud Transformer (AdaPT), a model designed to address the scalability issue posed by the quadratic scaling of point cloud transformers (PTs).
- AdaPT features an adaptive token selection mechanism that dynamically reduces the number of tokens during inference, enabling efficient processing of large point clouds.
- The authors also introduce a budget mechanism that allows for flexible adjustment of the computational cost of the model at inference time, without the need for retraining or fine-tuning separate models.
- Experimental evaluation shows that AdaPT significantly reduces computational complexity while maintaining competitive accuracy compared to standard PTs.