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Previous image based relighting methods require capturing multiple images toacquire high frequency lighting effect under different lighting conditions,which needs nontrivial effort and may be unrealistic in certain practical usescenarios. While such approaches rely entirely on cleverly sampling the colorimages under different lighting conditions, little has been done to utilizegeometric information that crucially influences the high-frequency features inthe images, such as glossy highlight and cast shadow. We therefore propose aframework for image relighting from a single flash photograph with itscorresponding depth map using deep learning. By incorporating the depth map,our approach is able to extrapolate realistic high-frequency effects undernovel lighting via geometry guided image decomposition from the flashlightimage, and predict the cast shadow map from the shadow-encoding transformeddepth map. Moreover, the single-image based setup greatly simplifies the datacapture process. We experimentally validate the advantage of our geometryguided approach over state-of-the-art image-based approaches in intrinsic imagedecomposition and image relighting, and also demonstrate our performance onreal mobile phone photo examples.

Towards Geometry Guided Neural Relighting with Flash Photography