Recent advancements in 3D Gaussian Splatting (3DGS) have shifted the focus toward balancing reconstruction fidelity with computational efficiency. In this work, we propose ImprovedGS+, a high-performance, low-level reinvention of the ImprovedGS strategy, implemented natively within the LichtFeld-Studio framework. By transitioning from high-level Python logic to hardware-optimized C++/CUDA kernels, we achieve a significant reduction in host-device synchronization and training latency. Our implementation introduces a Long-Axis-Split (LAS) CUDA kernel, custom Laplacian-based importance kernels with Non-Maximum Suppression (NMS) for edge scores, and an adaptive Exponential Scale Scheduler. Experimental results on the Mip-NeRF360 dataset demonstrate that ImprovedGS+ establishes a new Pareto-optimal front for scene reconstruction. Our 1M-budget variant outperforms the state-of-the-art MCMC baseline by achieving a 26.8% reduction in training time (saving 17 minutes per session) and utilizing 13.3% fewer Gaussians while maintaining superior visual quality. Furthermore, our full variant demonstrates a 1.28 dB PSNR increase over the ADC baseline with a 38.4% reduction in parametric complexity. These results validate ImprovedGS+ as a scalable, high-speed solution that upholds the core pillars of Speed, Quality, and Usability within the LichtFeld-Studio ecosystem.

翻译：近期，3D高斯泼溅（3DGS）的研究进展已转向在重建保真度与计算效率之间寻求平衡。本文提出ImprovedGS+，一种在LichtFeld-Studio框架内原生实现的高性能、底层重构的ImprovedGS策略。通过从高级Python逻辑转向硬件优化的C++/CUDA内核，我们显著减少了主机-设备同步开销与训练延迟。我们的实现引入了长轴分割（LAS）CUDA内核、基于自定义拉普拉斯算子的重要性内核（结合非极大值抑制（NMS）进行边缘评分）以及一种自适应指数尺度调度器。在Mip-NeRF360数据集上的实验结果表明，ImprovedGS+为场景重建建立了一个新的帕累托最优前沿。我们的1M预算变体在保持更优视觉质量的同时，相比最先进的MCMC基线实现了26.8%的训练时间缩减（每次训练节省17分钟）并减少了13.3%的高斯分布使用量。此外，我们的完整变体在参数复杂度降低38.4%的情况下，相比ADC基线实现了1.28 dB的PSNR提升。这些结果验证了ImprovedGS+作为一种可扩展的高速解决方案，在LichtFeld-Studio生态系统中坚守了速度、质量与可用性三大核心支柱。