Consumer-level applications require fast optimization of 3D Gaussian Splatting (3DGS) with high-fidelity novel view rendering. However, existing 3DGS acceleration approaches still incur substantial computation on redundant pixels while sacrificing fine details. In this paper, we present TurboGS, an error-guided training framework that accelerates 3DGS by concentrating optimization on perceptually informative pixels. TurboGS is built upon four core components: (1) a tile-wise sparse pixel sampling, which, driven by multi-view reconstruction errors during training, prioritizes challenging regions and skips well-reconstructed ones to avoid redundant gradient computation; (2) a tile-wise structure-aware loss with sparse Normalized Cross-Correlation, which provides sparse yet effective supervision to preserve fine details and stabilize training; (3) an error-driven Gaussian density control strategy, which dynamically allocates model capacity and removes redundant primitives; and (4) a tailored hybrid optimizer that couples Hessian-informed updates with Adam moment damping to stabilize and improve convergence under sparse supervision. Experiments on standard benchmarks demonstrate that TurboGS can deliver on par or superior rendering quality within 100 seconds on a single RTX 5090 GPU card (up to 10x training speedup over vanilla 3DGS).

翻译：消费级应用需要快速优化三维高斯泼溅（3DGS）以实现高保真新视角渲染。然而，现有3DGS加速方法在牺牲精细细节的同时，仍会在冗余像素上产生大量计算。本文提出TurboGS，一种基于误差引导的训练框架，通过聚焦于感知信息丰富的像素来加速3DGS。TurboGS基于四个核心组件构建：（1）分块稀疏像素采样，由训练期间多视角重建误差驱动，优先处理困难区域并跳过已良好重建的区域以避免冗余梯度计算；（2）基于稀疏归一化互相关的分块结构感知损失，提供稀疏而有效的监督以保持精细细节并稳定训练；（3）误差驱动的高斯密度控制策略，动态分配模型容量并移除冗余基元；（4）定制混合优化器，将海森信息更新与Adam矩阻尼耦合，以在稀疏监督下稳定并改善收敛。标准基准测试实验表明，TurboGS在单块RTX 5090 GPU上能在100秒内提供相当或更优的渲染质量（训练速度较原始3DGS提升最高10倍）。