用少量输入生成高质量可拼接3D瓦片，提升渲染效率
DAV-GSWT: Diffusion-Active-View Sampling for Data-Efficient Gaussian Splatting Wang Tiles
- 结合扩散模型与主动视角采样，自动选择关键视点
- 仅需少量观测数据即可生成无缝拼接的高保真瓦片
- 适合大规模虚拟环境构建，尤其数据受限场景
3D高斯瓦片的兴起彻底革新了神经渲染的逼真度，实现了复杂环境的高效合成。尽管最近已引入如王氏瓦片等程序化方法来生成广阔景观，但这些系统仍依赖密集的示例重建。本文提出DAV-GSWT，一种数据高效的框架，利用扩散先验和主动视角采样，从极少量输入中合成高保真高斯瓦片。通过分层不确定性量化机制与生成式扩散模型相结合，系统能自主识别最具信息量的视角，并补全缺失结构细节，确保瓦片间无缝过渡。实验表明，该方法显著减少所需数据量，同时保持视觉完整性与交互性能，满足大规模虚拟环境需求。
原文摘要 · Abstract (English)
The emergence of 3D Gaussian Splatting has fundamentally redefined the capabilities of photorealistic neural rendering by enabling high-throughput synthesis of complex environments. While procedural methods like Wang Tiles have recently been integrated to facilitate the generation of expansive landscapes, these systems typically remain constrained by a reliance on densely sampled exemplar reconstructions. We present DAV-GSWT, a data-efficient framework that leverages diffusion priors and active view sampling to synthesize high-fidelity Gaussian Splatting Wang Tiles from minimal input observations. By integrating a hierarchical uncertainty quantification mechanism with generative diffusion models, our approach autonomously identifies the most informative viewpoints while hallucinating missing structural details to ensure seamless tile transitions. Experimental results indicate that our system significantly reduces the required data volume while maintaining the visual integrity and interactive performance necessary for large-scale virtual environments.
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