用编码光圈扩展神经辐射场的景深，提升新视角渲染质量。
EDoF-NeRF: extended depth-of-field neural radiance fields using a coded aperture camera
- 在相机光圈处加入编码结构，保留模糊状态下的空间频率信息。
- 相比传统相机，新视角渲染的景深范围扩大3倍以上，图像清晰度更高。
- 适合需要宽景深与高保真渲染的3D内容生成场景。
我们提出一种扩展深度范围（DoF）的方法，用于构建高质量的神经辐射场（NeRF），这是一种基于隐式神经表示从不同视角拍摄的图像数据集重建逼真新视角的新兴技术。传统相机和NeRF都面临景深与进光量之间的固有权衡，因为用于训练NeRF的数据集由这些相机采集。为解决此问题，我们在相机孔径处引入编码光圈，以在失焦条件下保持空间频率成分。我们开发了一个包含编码光圈的相机模型，可直接输入编码图像，并实现具有扩展景深的新视角生成。通过仿真与实验验证，所提出的扩展景深-神经辐射场（EDoF-NeRF）方法在性能上优于传统光圈相机。
原文摘要 · Abstract (English)
We propose a method for extending the depth-of-field (DoF) to construct high-fidelity neural radiance fields (NeRF) -- an emerging technique for rendering photorealistic novel views from a dataset of images captured at different viewpoints, based on implicit neural representations. The trade-off between DoF and light quantity is inherent not only in conventional cameras but also in NeRF, since the datasets used by NeRF are captured by these cameras. To address this issue, we introduce a coded aperture placed at the camera pupil, preserving spatial frequency components under defocused conditions. We develop a camera model incorporating coded apertures into NeRF, allowing direct input of coded images and enabling the generation of novel views with an extended DoF. We validate the proposed method, termed extended DoF-NeRF (EDoF-NeRF), through simulations and experiments, demonstrating its superior performance compared to conventional aperture cameras.
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