NeRF是3D高斯溅射技术的重要助手

NeRF Is a Valuable Assistant for 3D Gaussian Splatting

July 31, 2025

作者: Shuangkang Fang, I-Chao Shen, Takeo Igarashi, Yufeng Wang, ZeSheng Wang, Yi Yang, Wenrui Ding, Shuchang Zhou

cs.AI

摘要

我们提出了NeRF-GS，一个新颖的框架，它联合优化了神经辐射场（NeRF）与三维高斯溅射（3DGS）。该框架利用NeRF固有的连续空间表示，有效缓解了3DGS的若干局限，包括对高斯初始化的敏感性、空间感知能力有限以及高斯间关联性弱等问题，从而提升了其性能。在NeRF-GS中，我们重新审视了3DGS的设计，逐步将其空间特征与NeRF对齐，使得两种表示能够通过共享的三维空间信息在同一场景中进行优化。此外，我们通过优化隐式特征和高斯位置的残差向量，进一步处理了两种方法之间的形式差异，增强了3DGS的个性化能力。在基准数据集上的实验结果表明，NeRF-GS超越了现有方法，达到了最先进的性能。这一结果证实了NeRF与3DGS是互补而非竞争的关系，为结合3DGS与NeRF以实现高效三维场景表示的混合方法提供了新的见解。

English

We introduce NeRF-GS, a novel framework that jointly optimizes Neural Radiance Fields (NeRF) and 3D Gaussian Splatting (3DGS). This framework leverages the inherent continuous spatial representation of NeRF to mitigate several limitations of 3DGS, including sensitivity to Gaussian initialization, limited spatial awareness, and weak inter-Gaussian correlations, thereby enhancing its performance. In NeRF-GS, we revisit the design of 3DGS and progressively align its spatial features with NeRF, enabling both representations to be optimized within the same scene through shared 3D spatial information. We further address the formal distinctions between the two approaches by optimizing residual vectors for both implicit features and Gaussian positions to enhance the personalized capabilities of 3DGS. Experimental results on benchmark datasets show that NeRF-GS surpasses existing methods and achieves state-of-the-art performance. This outcome confirms that NeRF and 3DGS are complementary rather than competing, offering new insights into hybrid approaches that combine 3DGS and NeRF for efficient 3D scene representation.