Adaptive token selection for scalable point cloud transformers

Area of Science:

• Computer Vision
• Geometric Deep Learning
• Natural Language Processing

Background:

• 3D data acquisition is rapidly increasing, driving demand for efficient point cloud processing models.
• Transformers have shown success in natural language processing and are being adapted for point cloud tasks.
• Standard point cloud transformers (PTs) face scalability challenges due to quadratic complexity with point cloud size.

Purpose of the Study:

• To develop an efficient geometric deep learning model for processing large-scale 3D point clouds.
• To address the computational scalability limitations of existing point cloud transformers.
• To introduce a flexible mechanism for managing computational cost during inference.

Main Methods:

• Proposing the Adaptive Point Cloud Transformer (AdaPT), which integrates an adaptive token selection mechanism into standard PTs.
• Implementing a dynamic token reduction strategy during inference to handle large point clouds.
• Introducing a budget mechanism for flexible adjustment of computational cost without retraining.

Main Results:

• AdaPT significantly reduces computational complexity for large point cloud processing.
• The model maintains competitive accuracy compared to standard point cloud transformers.
• Experimental evaluations on point cloud classification tasks validate AdaPT's efficiency and performance.

Conclusions:

• AdaPT offers an efficient and scalable solution for point cloud processing using geometric deep learning.
• The adaptive token selection and budget mechanism enable flexible and computationally efficient inference.
• AdaPT represents a significant advancement in applying transformer architectures to large-scale 3D data.