用子像素重参数化提升3D高分辨率渲染质量与效率
SubSplat: High-Resolution Pixel-aligned 3DGS via Sub-pixel Gaussian Reparameterization
- 将主高斯分解为更细粒度的子像素单元，从低分辨率特征恢复结构密度
- 在RealEstate10K和ACID数据集上实现高保真渲染，计算成本仅线性增长
- 适合追求高分辨率3D生成且对算力敏感的应用场景
像素对齐的高斯点阵可实现高效通用的新视角合成。然而，高分辨率渲染面临关键权衡：提高输入分辨率虽能增强细节，但导致网络计算成本呈平方级上升；维持低分辨率输入虽稳定计算开销，却造成高斯密度不足并引入伪影。为此，我们提出SubSplat，引入子像素高斯重参数化器（SPGR），将主高斯细分为精细的几何单元，直接从低分辨率特征恢复结构密度。同时通过多视图特征聚合，有效捕捉高频细节。在RealEstate10K和ACID数据集上的实验表明，SubSplat实现了高保真渲染，并具备卓越效率。结果验证了该框架成功解决了像素对齐高斯点阵中重参数化精度与网络计算成本之间的固有权衡。
原文摘要 · Abstract (English)
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.
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