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Geometry-Guided Reinforcement Learning for Multi-view Consistent 3D Scene Editing
arXiv – CS AI|Jiyuan Wang, Chunyu Lin, Lei Sun, Zhi Cao, Yuyang Yin, Lang Nie, Zhenlong Yuan, Xiangxiang Chu, Yunchao Wei, Kang Liao, Guosheng Lin||1 views
🤖AI Summary
Researchers propose RL3DEdit, a reinforcement learning framework that addresses multi-view consistency challenges in 3D scene editing by using 2D diffusion model priors with novel reward signals from 3D foundation models. The method achieves stable multi-view consistency and outperforms existing approaches in editing quality and efficiency.
Key Takeaways
- →RL3DEdit uses reinforcement learning to solve multi-view consistency problems in 3D editing where supervised fine-tuning is infeasible due to data scarcity.
- →The framework leverages VGGT foundation model's confidence maps and pose estimation errors as reward signals to guide 2D editing priors onto 3D-consistent manifolds.
- →Experimental results show the method outperforms state-of-the-art approaches in both editing quality and computational efficiency.
- →The researchers will release code and models to promote further development in 3D editing research.
- →The approach addresses a key limitation in current 3D editing methods by making verification of 3D consistency more tractable than generation.
#3d-editing#reinforcement-learning#diffusion-models#computer-vision#3d-consistency#foundation-models#scene-editing#multiview
Read Original →via arXiv – CS AI
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