Z-Order Transformer for Feed-Forward Gaussian Splatting

• URL: http://arxiv.org/abs/2605.13465v1
• Date: Wed, 13 May 2026 12:54:59 GMT
• Title: Z-Order Transformer for Feed-Forward Gaussian Splatting
• Abstract summary: We introduce a transformer-based architecture specifically designed for feed-forward Gaussian Splatting.<n>Our key insight is that spatial and semantic relationships among Gaussians can be effectively captured through a sparse attention mechanism.<n>We incorporate this Z-order strategy to adaptively suppress redundancy while preserving critical structural details.
• Score: 14.297614617317533
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Recent advances in 3D Gaussian Splatting (3DGS) have enabled significant progress in photorealistic novel view synthesis. However, traditional 3DGS relies on a slow, iterative optimization process, which limits its use in scenarios demanding real-time results. To overcome this bottleneck, recent feed-forward methods aim to predict Gaussian attributes directly from images, but they often struggle with the redundancy of Gaussian primitives and rendering quality. In this work, we introduce a transformer-based architecture specifically designed for feed-forward Gaussian Splatting. Our key insight is that spatial and semantic relationships among Gaussians can be effectively captured through a sparse attention mechanism, enabled by a Z-order strategy that organizes the unstructured Gaussian set into a spatially coherent sequence. Furthermore, we incorporate this Z-order strategy to adaptively suppress redundancy while preserving critical structural details. This allows the transformer to efficiently model context, compress Gaussian primitives, and predict Gaussian attributes in a single forward pass. Comprehensive experiments demonstrate that our method achieves fast and high-quality novel view synthesis with fewer Gaussian primitives.

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