NeRFtrinsic Four：一种联合优化多样内参与外参相机参数的端到端可训练NeRF
NeRFtrinsic Four: An End-To-End Trainable NeRF Jointly Optimizing Diverse Intrinsic and Extrinsic Camera Parameters
AI总结：
提出NeRFtrinsic Four，一种端到端可训练的NeRF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内参，在LLFF和BLEFF数据集上优于现有方法，并引入了新数据集iFF。
摘要：
使用神经辐射场（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相机参数的先验知识。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。当前关于相机参数和NeRF联合优化的研究集中在精炼带噪声的外参相机参数上，并且通常依赖于内参相机参数的预处理。进一步的方法仅限于覆盖单一相机内参。为了解决这些限制，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来估计外参相机参数，并通过投影误差的监督来动态预测变化的内参相机参数。我们的方法在LLFF和BLEFF上优于现有的联合优化方法。除了这些现有数据集之外，我们还引入了一个名为iFF的具有变化内参相机参数的新数据集。NeRFtrinsic Four在基于NeRF联合优化的视角合成方面迈出了一步，并使得在具有变化相机参数的真实世界场景中能够进行更真实和灵活的渲染。
英文摘要：
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.
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