Recent advancements in Gaussian Splatting (3DGS) have introduced various modifications to the original kernel, resulting in significant performance improvements. However, many of these kernel changes are incompatible with existing datasets optimized for the original Gaussian kernel, presenting a challenge for widespread adoption. In this work, we address this challenge by proposing an alternative kernel that maintains compatibility with existing datasets while improving computational efficiency. Specifically, we replace the original exponential kernel with a polynomial approximation combined with a ReLU function. This modification allows for more aggressive culling of Gaussians, leading to enhanced performance across different 3DGS implementations. Our results show a notable performance improvement of 4 to 15% with negligible impact on image quality. We also provide a detailed mathematical analysis of the new kernel and discuss its potential benefits for 3DGS implementations on NPU hardware.

翻译：近期高斯泼溅（3DGS）技术的进展为原始核引入了多种改进方案，显著提升了性能表现。然而，许多核的变更与现有针对原始高斯核优化的数据集存在兼容性问题，制约了其广泛应用。针对这一挑战，本文提出了一种替代核方案，既能保持与现有数据集的兼容性，又能提升计算效率。具体而言，我们用多项式近似配合ReLU函数替代原始指数型核。该改进可对高斯体进行更激进的筛选优化，从而在多种3DGS实现中提升性能。实验结果表明，该方法在图像质量影响极小的情况下实现了4%至15%的显著性能提升。我们同时对该新型核进行了详尽的数学分析，并探讨其在NPU硬件中优化3DGS实现的潜在优势。