An Adapter-free Fine-tuning Approach for Tuning 3D Foundation Models

• URL: http://arxiv.org/abs/2603.23730v1
• Date: Tue, 24 Mar 2026 21:28:37 GMT
• Title: An Adapter-free Fine-tuning Approach for Tuning 3D Foundation Models
• Abstract summary: We propose Momentum-Consistency Fine-Tuning (MCFT) to bridge the gap between full and parameter-efficient fine-tuning.<n>MCFT selectively fine-tunes a portion of the pre-trained encoder while enforcing a momentum-based consistency constraint.<n>We extend MCFT with two variants: a semi-supervised framework that leverages abundant unlabeled data to enhance few-shot performance, and a pruning-based variant that improves computational efficiency.
• Score: 22.63554774942836
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Point cloud foundation models demonstrate strong generalization, yet adapting them to downstream tasks remains challenging in low-data regimes. Full fine-tuning often leads to overfitting and significant drift from pre-trained representations, while existing parameter-efficient fine-tuning (PEFT) methods mitigate this issue by introducing additional trainable components at the cost of increased inference-time latency. We propose Momentum-Consistency Fine-Tuning (MCFT), an adapter-free approach that bridges the gap between full and parameter-efficient fine-tuning. MCFT selectively fine-tunes a portion of the pre-trained encoder while enforcing a momentum-based consistency constraint to preserve task-agnostic representations. Unlike PEFT methods, MCFT introduces no additional representation learning parameters beyond a standard task head, maintaining the original model's parameter count and inference efficiency. We further extend MCFT with two variants: a semi-supervised framework that leverages abundant unlabeled data to enhance few-shot performance, and a pruning-based variant that improves computational efficiency through structured layer removal. Extensive experiments on object recognition and part segmentation benchmarks demonstrate that MCFT consistently outperforms prior methods, achieving a 3.30% gain in 5-shot settings and up to a 6.13% improvement with semi-supervised learning, while remaining well-suited for resource-constrained deployment.

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