Novel view synthesis using neural radiance fields (NeRF) is the state-of-the-art technique for generating high-quality images from novel viewpoints. Existing methods require a priori knowledge about extrinsic and intrinsic camera parameters. This limits their applicability to synthetic scenes, or real-world scenarios with the necessity of a preprocessing step. Current research on the joint optimization of camera parameters and NeRF focuses on refining noisy extrinsic camera parameters and often relies on the preprocessing of intrinsic camera parameters. Further approaches are limited to cover only one single camera intrinsic. To address these limitations, we propose a novel end-to-end trainable approach called NeRFtrinsic Four. We utilize Gaussian Fourier features to estimate extrinsic camera parameters and dynamically predict varying intrinsic camera parameters through the supervision of the projection error. Our approach outperforms existing joint optimization methods on LLFF and BLEFF. In addition to these existing datasets, we introduce a new dataset called iFF with varying intrinsic camera parameters. NeRFtrinsic Four is a step forward in joint optimization NeRF-based view synthesis and enables more realistic and flexible rendering in real-world scenarios with varying camera parameters.

翻译：摘要：利用神经辐射场（NeRF）进行新视角合成是从新视角生成高质量图像的最先进技术。现有方法需要关于外参和内参的先验知识，这限制了它们在合成场景或需要预处理步骤的真实世界场景中的适用性。当前关于相机参数与NeRF联合优化的研究主要集中于优化含噪声的外参，且通常依赖内参的预处理。此外，现有方法仅能处理单一相机内参。为解决这些局限，我们提出了一种名为NeRFtrinsic Four的新型端到端可训练方法。我们利用高斯傅里叶特征估计外参，并通过投影误差监督动态预测变化的内参。我们的方法在LLFF和BLEFF数据集上优于现有联合优化方法。除现有数据集外，我们还引入了名为iFF的新数据集，其内参动态变化。NeRFtrinsic Four在基于NeRF的联合优化视角合成领域迈出了重要一步，能够在相机参数动态变化的真实场景中实现更逼真、更灵活的渲染。