发表机构
AimFuture(c); Sungkyunkwan University; Korea University（AimFuture公司; 成均馆大学; 韩国大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究像素对齐高斯渲染高分辨率与成本权衡问题，提出SubSplat方法，通过亚像素高斯重参数化器细分高斯并结合特征聚合提升质量，实验证明该方法能高效实现高保真渲染，解决了相关权衡问题。
AI 中文摘要
像素对齐的高斯渲染能够实现高效且通用的新视图合成。然而，高分辨率渲染面临关键权衡：提高输入分辨率虽能提升细节，但网络计算成本会二次上升；保持低分辨率输入可稳定成本，却会导致高斯密度不足和伪影。为解决此问题，我们提出SubSplat，引入亚像素高斯重参数化器将主高斯细分为细粒度基元，直接从低分辨率特征恢复结构密度。通过特征聚合进一步提升重参数化质量，有效捕捉多视图高频细节。在RealEstate10K和ACID上的实验表明，SubSplat以卓越效率实现高保真渲染，成功解决了像素对齐高斯渲染中重参数化保真度与网络计算成本之间的权衡。
英文摘要
Pixel-aligned Gaussian splatting enables efficient and generalizable novel-view synthesis. However, high-resolution rendering faces a critical trade-off where increasing input resolution improves detail at the expense of quadratically rising network computational cost. Conversely, maintaining low-resolution inputs stabilizes this cost but results in insufficient Gaussian density and artifacts. To address this, we propose SubSplat, which introduces Sub-pixel Gaussian Reparameterizer(SPGR) to subdivide primary Gaussians into fine-grained primitives, restoring structural density directly from low-resolution features. We further enhance the reparameterization quality through feature aggregation, which effectively captures high-frequency details across multiple views. Experiments on RealEstate10K and ACID demonstrate that SubSplat achieves high-fidelity rendering with superior efficiency. Our results validate that the proposed framework successfully resolves the trade-off between reparameterization fidelity and network computational cost inherent in pixel-aligned Gaussian Splatting.
CommentsECCV 2026 (Spotlight)
