AI RESEARCH
An Adapter-free Fine-tuning Approach for Tuning 3D Foundation Models
arXiv CS.CV
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ArXi:2603.23730v1 Announce Type: new Point cloud foundation models nstrate 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