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.

翻译：近年来，神经辐射场的发展使得从多视角相机数据实现前所未有的照片级真实感三维场景与物体重建成为可能。然而，现有方法采用过度简化的针孔相机模型，导致散焦模糊被“固化”到重建的辐射场中。我们提出一种改进的光线投射方法，利用镜头光学特性来增强存在散焦模糊时的场景重建质量。这使得我们能够基于具有有限孔径的实用相机测量数据，提升辐射场重建的质量。我们证明，所提出的模型比针孔模型及其他散焦模糊近似模型更准确地匹配实用相机的散焦模糊特性，尤其在存在部分遮挡的情况下。该方法使我们能够获得更清晰的重建结果，在全聚焦图像的验证集上，PSNR在合成与真实数据集上均提升高达3 dB。