Domain Generalizable Adaptation of 3D Vision-Language Models via Regularized Fine-Tuning

2026-06-16 · Source: Computer Vision and Pattern Recognition · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems · Depth: Expert, quick

Summary

ReFine3D is a novel regularized fine-tuning framework designed to enhance domain generalizability for 3D large multimodal models (LMMs), which often struggle with overfitting and catastrophic forgetting when adapting to new domains with limited data. This framework integrates selective layer tuning with two key regularization strategies: multi-view consistency across augmented point clouds and text diversity achieved through synonym-based prompts generated by large language models. Additionally, ReFine3D incorporates point-rendered vision supervision and a test-time augmentation mechanism utilizing confidence-based aggregation to boost robustness. Extensive experiments demonstrate that ReFine3D improves base-to-novel class generalization by 1.36%, cross-dataset transfer by 2.43%, robustness to corruption by 1.80%, and few-shot accuracy by up to 3.11%, surpassing previous methods with minimal computational overhead.

Key takeaway

For Machine Learning Engineers adapting 3D large multimodal models to new domains with limited data, ReFine3D offers a robust framework to mitigate overfitting and catastrophic forgetting. You should consider integrating its selective layer tuning, multi-view consistency, and LLM-driven text diversity regularization strategies. This approach can significantly improve your model's generalization across datasets, robustness to corruption, and few-shot accuracy with minimal computational overhead.

Key insights

ReFine3D regularizes 3D LMM fine-tuning with multi-view consistency and text diversity to prevent overfitting and enhance domain generalization.

Principles

• Selective layer tuning prevents forgetting.
• Multi-view consistency boosts robustness.
• Text diversity improves generalization.

Method

ReFine3D employs selective layer tuning, multi-view consistency, and LLM-generated text diversity regularization. It adds point-rendered vision supervision and confidence-based test-time augmentation for robust 3D LMM adaptation.

In practice

• Apply selective layer tuning.
• Implement multi-view consistency.
• Use LLMs for prompt diversity.

Topics

• 3D Vision
• Large Multimodal Models
• Domain Generalization
• Fine-tuning
• Regularization
• Point Clouds
• Test-Time Augmentation

Best for: Research Scientist, AI Scientist, Machine Learning Engineer, Computer Vision Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Computer Vision and Pattern Recognition.