用多项式核替代高斯点阵原核，提升效率且兼容旧数据集。
From ex(p) to poly: Gaussian Splatting with Polynomial Kernels
- 用多项式+ReLU替换原指数核，实现更高效裁剪。
- 性能提升4%~15%，图像质量几乎不变。
- 适合希望兼容旧数据并优化推理速度的开发者。
最近的高斯点阵（3DGS）进展通过修改原始核函数带来了显著性能提升。然而，许多核函数变更与为原始高斯核优化的现有数据集不兼容，阻碍了广泛应用。本文提出一种新核函数，在保持与现有数据集兼容的同时提升计算效率。具体而言，将原指数核替换为多项式近似结合ReLU函数。该修改支持更激进的高斯点裁剪，从而在不同3DGS实现中均获得性能提升。实验结果表明，性能提升达4%至15%，对图像质量影响可忽略。我们还提供了新核函数的详细数学分析，并讨论其在NPU硬件上的潜在优势。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。