Mining Attribute Subspaces for Efficient Fine-tuning of 3D Foundation Models 事件

Mining Attribute Subspaces for Efficient Fine-tuning of 3D Foundation Models arXiv:2604.10095v2 Announce Type: replace Abstract: With the emergence of 3D foundation models, there is growing interest in fine-tuning them for downstream tasks, where LoRA is the dominant fine-tuning paradigm. As 3D datasets exhibit distinct variations in texture, geometry, camera motion, and lighting, there are interesting fundamental questions: 1) Are there LoRA subspaces associated with each type of variation? 2)