NeRF Is a Valuable Assistant for 3D Gaussian Splatting
Authors: name: Shuangkang Fang, name: I-Chao Shen, name: Takeo Igarashi, name: Yufeng Wang, name: ZeSheng Wang, name: Yi Yang, name: Wenrui Ding, name: Shuchang Zhou

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.