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

2026-07-23 · Source: cs.CV updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems, Software Development & Engineering · Depth: Expert, extended

Summary

DAV-GSWT is a novel data-efficient framework designed to synthesize high-fidelity Gaussian Splatting Wang Tiles (GSWT) for expansive 3D landscapes using minimal input observations. It addresses the common limitation of GSWT systems, which typically require densely sampled exemplar reconstructions. The framework integrates hierarchical uncertainty quantification with generative diffusion models, specifically using Zero-1-to-3 XL v2 as a prior, to autonomously identify the most informative viewpoints. This active view sampling, combined with diffusion-based refinement, hallucinates missing structural details and ensures seamless tile transitions. Experimental results, conducted on an NVIDIA RTX 4090 GPU, demonstrate that DAV-GSWT significantly reduces the required data volume, starting from 8 initial views and acquiring 20 views per iteration over 3 iterations, while maintaining visual integrity and interactive performance for large-scale virtual environments.

Key takeaway

For Computer Vision Engineers developing large-scale virtual environments or robotic simulations, DAV-GSWT offers a critical solution to the data acquisition bottleneck. You should consider integrating active perception with generative diffusion models to drastically reduce the input data needed for photorealistic 3D terrain generation. This approach allows you to achieve high-fidelity, seamlessly tiled landscapes with significantly fewer physical captures, accelerating development and deployment in data-constrained scenarios.

Key insights

DAV-GSWT synthesizes expansive 3D Gaussian Splatting Wang Tiles from sparse data using active view sampling and diffusion priors.

Principles

• Uncertainty-driven view selection enhances reconstruction fidelity.
• Generative diffusion models can hallucinate missing 3D details.
• Semantic-aware graph cuts improve tile seam continuity.

Method

DAV-GSWT starts with a coarse Gaussian field, then iteratively selects top-k uncertain viewpoints using diffusion priors and uncertainty metrics (image-space LPIPS gradients or latent 2-Wasserstein divergence) for physical acquisition, refining the field and optimizing tile seams.

In practice

• Use Zero-1-to-3 XL v2 for diffusion priors.
• Combine Wasserstein-2 and LPIPS for robust uncertainty.
• Apply semantic segmentation (SAM v2) for better seam stitching.

Topics

• 3D Gaussian Splatting
• Wang Tiles
• Diffusion Priors
• Active View Selection
• Uncertainty Quantification
• Procedural Generation
• Data Efficiency

Best for: Research Scientist, AI Scientist, Machine Learning Engineer, Computer Vision Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.CV updates on arXiv.org.