We propose a method for reconstructing a continuous light field of a target scene from a single observed image. Our method takes the best of two worlds: joint aperture-exposure coding for compressive light-field acquisition, and a neural radiance field (NeRF) for view synthesis. Joint aperture-exposure coding implemented in a camera enables effective embedding of 3-D scene information into an observed image, but in previous works, it was used only for reconstructing discretized light-field views. NeRF-based neural rendering enables high quality view synthesis of a 3-D scene from continuous viewpoints, but when only a single image is given as the input, it struggles to achieve satisfactory quality. Our method integrates these two techniques into an efficient and end-to-end trainable pipeline. Trained on a wide variety of scenes, our method can reconstruct continuous light fields accurately and efficiently without any test time optimization. To our knowledge, this is the first work to bridge two worlds: camera design for efficiently acquiring 3-D information and neural rendering.

翻译：我们提出了一种从单张观测图像中重建目标场景连续光场的方法。该方法融合了两种技术的优势：用于压缩光场采集的联合孔径-曝光编码技术，以及用于视角合成的神经辐射场（NeRF）技术。相机中实现的联合孔径-曝光编码能够将三维场景信息有效嵌入到观测图像中，但此前的研究仅将其用于重建离散化的光场视角。基于NeRF的神经渲染技术可从连续视角实现高质量的三维场景视角合成，但当仅以单张图像作为输入时，其效果难以令人满意。本方法将这两种技术整合到一条高效且端到端可训练的处理流程中。经过多种场景的训练后，该方法无需任何测试时优化即可准确高效地重建连续光场。据我们所知，这是首次在高效采集三维信息的相机设计与神经渲染之间架起桥梁的研究工作。