NeRF 是 3D 高斯泼溅技术的重要助手

摘要

我們提出了NeRF-GS，這是一個新穎的框架，能夠同時優化神經輻射場（NeRF）和3D高斯潑濺（3DGS）。該框架利用NeRF固有的連續空間表示來緩解3DGS的若干限制，包括對高斯初始化的敏感性、有限的空間感知能力以及高斯間弱相關性，從而提升其性能。在NeRF-GS中，我們重新審視了3DGS的設計，並逐步將其空間特徵與NeRF對齊，使這兩種表示能夠通過共享的3D空間信息在同一場景中進行優化。我們進一步通過優化隱式特徵和高斯位置的殘差向量來解決兩種方法之間的形式差異，從而增強3DGS的個性化能力。在基準數據集上的實驗結果顯示，NeRF-GS超越了現有方法，達到了最先進的性能。這一結果證實了NeRF和3DGS是互補而非競爭的，為結合3DGS和NeRF的高效3D場景表示提供了新的混合方法見解。

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.

NeRF 是 3D 高斯泼溅技术的重要助手

NeRF Is a Valuable Assistant for 3D Gaussian Splatting

摘要

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