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：神经辐射场（NeRF）在新型视角合成中，是生成高质量图像的最先进技术。现有方法需预先获取相机的外参和内参，这限制了其在合成场景以及需要预处理步骤的真实场景中的应用。当前关于相机参数与NeRF联合优化的研究，主要聚焦于修正含噪声的外参，且通常依赖内参的预处理；进一步的方法也仅限于处理单一相机内参。为解决这些局限，我们提出一种名为NeRFtrinsic Four的新型端到端可训练方法。该方法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。在LLFF和BLEFF数据集上，我们的方法优于现有联合优化方法。除现有数据集外，我们引入了一个内参可变的新数据集iFF。NeRFtrinsic Four在基于NeRF的联合优化视角合成领域迈出一步，得以在相机参数变化的真实场景中实现更逼真、更灵活的渲染。