DAV-GSWT: Diffusion-Active-View Sampling for Data-Efficient Gaussian Splatting Wang Tiles

• URL: http://arxiv.org/abs/2602.15355v1
• Date: Tue, 17 Feb 2026 04:47:39 GMT
• Title: DAV-GSWT: Diffusion-Active-View Sampling for Data-Efficient Gaussian Splatting Wang Tiles
• Authors: Rong Fu, Jiekai Wu, Haiyun Wei, Yee Tan Jia, Wenxin Zhang, Yang Li, Xiaowen Ma, Wangyu Wu, Simon Fong,
• Abstract summary: 3D Gaussian Splatting has redefined the capabilities of photorealistic neural rendering.<n>DAV-GSWT is a framework that leverages diffusion priors and active view sampling to synthesize high-fidelity Wang Tiles.<n>Our system significantly reduces the required data volume while maintaining the visual integrity and interactive performance necessary for large-scale virtual environments.
• Score: 9.641815204004823
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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