Light fields (LFs), conducive to comprehensive scene radiance recorded across
angular dimensions, find wide applications in 3D reconstruction, virtual
reality, and computational photography.However, the LF acquisition is
inevitably time-consuming and resource-intensive due to the mainstream
acquisition strategy involving manual capture or laborious software
synthesis.Given such a challenge, we introduce LFdiff, a straightforward yet
effective diffusion-based generative framework tailored for LF synthesis, which
adopts only a single RGB image as input.LFdiff leverages disparity estimated by
a monocular depth estimation network and incorporates two distinctive
components: a novel condition scheme and a noise estimation network tailored
for LF data.Specifically, we design a position-aware warping condition scheme,
enhancing inter-view geometry learning via a robust conditional signal.We then
propose DistgUnet, a disentanglement-based noise estimation network, to harness
comprehensive LF representations.Extensive experiments demonstrate that LFdiff
excels in synthesizing visually pleasing and disparity-controllable light
fields with enhanced generalization capability.Additionally, comprehensive
results affirm the broad applicability of the generated LF data, spanning
applications like LF super-resolution and refocusing.

通过仅使用单个 RGB 图像作为输入，LFdiff 采用基于扩散的生成框架针对光场合成进行设计，利用单目深度估计网络估计视差并结合新颖的条件方案和面向光场数据的噪声估计网络。实验结果表明，LFdiff 在综合光场合成方面具有卓越表现，并且生成的光场数据具有广泛的应用性，涵盖了光场超分辨率和重焦等应用领域。