In recent years, the development of Neural Radiance Fields has enabled a
previously unseen level of photo-realistic 3D reconstruction of scenes and
objects from multi-view camera data. However, previous methods use an
oversimplified pinhole camera model resulting in defocus blur being `baked'
into the reconstructed radiance field. We propose a modification to the ray
casting that leverages the optics of lenses to enhance scene reconstruction in
the presence of defocus blur. This allows us to improve the quality of radiance
field reconstructions from the measurements of a practical camera with finite
aperture. We show that the proposed model matches the defocus blur behavior of
practical cameras more closely than pinhole models and other approximations of
defocus blur models, particularly in the presence of partial occlusions. This
allows us to achieve sharper reconstructions, improving the PSNR on validation
of all-in-focus images, on both synthetic and real datasets, by up to 3 dB.

通过修改光线投射模型以利用透镜光学，我们提出了一种改进的模型，并且能够更好地匹配实际摄像机的虚焦模糊行为，特别是在部分遮挡的情况下，从而实现更锐利的重建，将全焦点图像的峰值信噪比在合成和真实数据集上提高了最多 3 dB。