The emerging Neural Radiance Field (NeRF) shows great potential in representing 3D scenes, which can render photo-realistic images from novel view with only sparse views given. However, utilizing NeRF to reconstruct real-world scenes requires images from different viewpoints, which limits its practical application. This problem can be even more pronounced for large scenes. In this paper, we introduce a new task called NeRF synthesis that utilizes the structural content of a NeRF patch exemplar to construct a new radiance field of large size. We propose a two-phase method for synthesizing new scenes that are continuous in geometry and appearance. We also propose a boundary constraint method to synthesize scenes of arbitrary size without artifacts. Specifically, we control the lighting effects of synthesized scenes using shading guidance instead of decoupling the scene. We have demonstrated that our method can generate high-quality results with consistent geometry and appearance, even for scenes with complex lighting. We can also synthesize new scenes on curved surface with arbitrary lighting effects, which enhances the practicality of our proposed NeRF synthesis approach.

翻译：新兴的神经辐射场（NeRF）在表示三维场景方面展现出巨大潜力，能够仅凭稀疏视角输入从新视角渲染出照片级真实图像。然而，利用NeRF重建真实场景需要从不同视角获取图像，这限制了其实际应用。对于大规模场景而言，这一问题尤为突出。本文提出一种名为NeRF合成的新任务，旨在利用NeRF图像块样本的结构内容构建大规模新辐射场。我们提出两阶段方法合成几何与外观连续的新场景，并设计边界约束方法以无伪影方式合成任意尺度场景。具体而言，我们通过着色引导而非解耦场景的方式控制合成场景的光照效果。实验证明，即使对于具有复杂光照的场景，该方法也能生成几何与外观一致的高质量结果。此外，我们还能在曲面上合成具有任意光照效果的新场景，从而增强了所提出的NeRF合成方法的实用性。