Adaptive Point-Prompt Tuning: Fine-Tuning Heterogeneous Foundation Models for 3D Point Cloud Analysis.

Area of Science:

• Computer Vision
• Machine Learning
• 3D Data Analysis

Background:

• Parameter-efficient fine-tuning is effective for 1D and 2D foundation models.
• Pre-training large 3D models is challenging due to limited point cloud data.
• Existing "high-to-low" mapping methods for 3D lack generalizability and lose spatial information.

Purpose of the Study:

• To develop a generalizable framework for adapting foundation models of any modality to 3D point cloud analysis.
• To directly leverage point features for calibrating heterogeneous foundation models.
• To enable efficient fine-tuning of large models for 3D tasks without losing spatial geometry.

Main Methods:

• Proposed Adaptive Point-Prompt Tuning (APPT) for parameter-efficient fine-tuning.
• Converted point clouds to point embeddings by aggregating local geometry and using linear layers.
• Introduced a permutation-invariant feature for point tokens to capture relative positions and optimize self-attention.
• Developed a prompt generator for dynamic point-prompt creation without additional parameters.

Main Results:

• APPT enables direct point cloud processing without heterogeneous mappings.
• Achieved effective performance on various downstream 3D point cloud analysis tasks.
• Demonstrated high efficiency by fine-tuning only 3.8% of trainable parameters.
• Preserved spatial geometries and provided rich global structural information.

Conclusions:

• APPT offers an efficient and generalizable solution for adapting foundation models to 3D point cloud analysis.
• The method effectively addresses challenges of data scarcity and spatial information loss in 3D deep learning.
• APPT significantly reduces computational overhead while maintaining high performance across diverse benchmarks.