EDoF-NeRF: extended depth-of-field neural radiance fields using a coded aperture camera
This work addresses the depth-of-field versus light quantity trade-off in NeRF, benefiting 3D scene reconstruction and novel view synthesis applications.
EDoF-NeRF extends the depth-of-field of neural radiance fields by using a coded aperture camera, enabling high-fidelity novel view synthesis with extended DoF. Simulations and experiments show superior performance over conventional aperture cameras.
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.