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We present a machine learning algorithm that takes as input a 2D RGB imageand synthesizes a 4D RGBD light field (color and depth of the scene in each raydirection). For training, we introduce the largest public light field dataset,consisting of over 3300 plenoptic camera light fields of scenes containingflowers and plants. Our synthesis pipeline consists of a convolutional neuralnetwork (CNN) that estimates scene geometry, a stage that renders a Lambertianlight field using that geometry, and a second CNN that predicts occluded raysand non-Lambertian effects. Our algorithm builds on recent view synthesismethods, but is unique in predicting RGBD for each light field ray andimproving unsupervised single image depth estimation by enforcing consistencyof ray depths that should intersect the same scene point. Please see oursupplementary video at https://youtu.be/yLCvWoQLnms

Learning to Synthesize a 4D RGBD Light Field from a Single Image