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About this paper
Kangjie Chen, Yingji Zhong, Zhihao Li, Jiaqi Lin, Youyu Chen, Minghan Qin, Haoqian Wang
This work identifies and quantifies "co-adaptation" as a core problem causing appearance artifacts in sparse-view 3D Gaussian Splatting, proposing Gaussian dropout and multiplicative opacity noise injection as effective regularization techniques. These methods reduce Gaussian entanglement, leading to more photorealistic novel view synthesis with improved metrics (e.g., higher PSNR, SSIM) and enhanced geometric coherence.
Key insights
- Co-adaptation, characterized by overly interdependent Gaussians, is a primary driver of appearance artifacts in sparse-view 3DGS, directly impacting generalization to novel views.
- The Co-Adaptation Score (CA) effectively quantifies this Gaussian entanglement, demonstrating a clear correlation between lower CA and improved rendering quality.
- Simple, plug-and-play regularization strategies like Gaussian dropout and opacity noise can effectively alleviate co-adaptation, leading to more robust and visually consistent 3D representations without excessive computational overhead.
Summary by alphaXiv.
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