${C}^{3}$-GS: Learning Context-aware, Cross-dimension, Cross-scale Feature for Generalizable Gaussian Splatting

• URL: http://arxiv.org/abs/2508.20754v1
• Date: Thu, 28 Aug 2025 13:12:18 GMT
• Title: ${C}^{3}$-GS: Learning Context-aware, Cross-dimension, Cross-scale Feature for Generalizable Gaussian Splatting
• Authors: Yuxi Hu, Jun Zhang, Kuangyi Chen, Zhe Zhang, Friedrich Fraundorfer,
• Abstract summary: Generalizable Gaussian Splatting aims to synthesize novel views for unseen scenes without per-scene optimization.<n>We propose $mathbfC3$-GS, a framework that enhances feature learning by incorporating context-aware, cross-dimension, and cross-scale constraints.<n>Our architecture integrates three lightweight modules into a unified rendering pipeline, improving feature fusion and enabling synthesis without requiring additional supervision.
• Score: 16.868578618340262
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Generalizable Gaussian Splatting aims to synthesize novel views for unseen scenes without per-scene optimization. In particular, recent advancements utilize feed-forward networks to predict per-pixel Gaussian parameters, enabling high-quality synthesis from sparse input views. However, existing approaches fall short in encoding discriminative, multi-view consistent features for Gaussian predictions, which struggle to construct accurate geometry with sparse views. To address this, we propose $\mathbf{C}^{3}$-GS, a framework that enhances feature learning by incorporating context-aware, cross-dimension, and cross-scale constraints. Our architecture integrates three lightweight modules into a unified rendering pipeline, improving feature fusion and enabling photorealistic synthesis without requiring additional supervision. Extensive experiments on benchmark datasets validate that $\mathbf{C}^{3}$-GS achieves state-of-the-art rendering quality and generalization ability. Code is available at: https://github.com/YuhsiHu/C3-GS.

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