Cross-Modality Feature Aggregation for Cross-Domain Point Cloud Representation Learning.

Area of Science:

• Computer Vision
• Machine Learning
• 3D Data Analysis

Background:

• Current 3D point cloud representation learning methods often suffer from performance degradation due to domain shifts between training and testing datasets.
• Models trained on single datasets tend to overfit, lacking robustness when applied to new, unseen domains.
• Existing cross-domain approaches struggle with the inherent lack of semantic information in point clouds, limiting their generalization capabilities.

Purpose of the Study:

• To develop a robust cross-domain 3D point cloud representation learning method that overcomes domain shift challenges.
• To improve the transferability and generalization of 3D point cloud models across diverse datasets.
• To leverage semantic information from multi-view images to enhance 3D point cloud feature learning.

Main Methods:

• Introduced 3D-CFA, a cross-modality feature aggregation method combining geometry and semantic tokens.
• Utilized a modality transformation module to convert 3D point clouds into multi-view images.
• Employed a cross-modal projector to generate transferable 3D tokens by integrating geometry and semantic encoders.
• Incorporated an elastic domain alignment module for learning domain-invariant features.

Main Results:

• 3D-CFA effectively aggregates geometry and semantic tokens, creating more transferable features for cross-domain learning.
• Semantic tokens derived from multi-view images act as a bridge to 2D foundation models, significantly improving cross-domain generalization.
• The elastic domain alignment module facilitates domain adaptation and generalization by learning hierarchical domain-invariant features.
• Experimental results on multiple benchmarks show superior performance compared to state-of-the-art methods.

Conclusions:

• 3D-CFA offers an effective solution for cross-domain 3D point cloud representation learning by bridging 2D and 3D modalities.
• The method successfully transfers knowledge from large-scale pre-trained 2D foundation models with minimal trainable parameters.
• 3D-CFA demonstrates significant improvements in handling severe domain shifts, paving the way for more robust 3D data analysis.