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hello everyone my name is Ian Im presenting our work neurolight field estimation for Street scenes with differentiable virtual object insertion this is a joint word with wenjung David Yan and Sanya the task of lighting estimation aims to understand the lighting distribution across the scene that is to estimate the 5D light field function lighting estimation can bring synthetic assets into real images for example we can create photorealistic insertion of synthetic contents we can also use object insertion as a data augmentation the exerted objects come naturally with free labels and can be used to train perception models such as a 3D detector prior works also approach to lighting estimation whole geofra at all learn to predict the sky model at Infinity it handles the HDR level but cannot produce location-dependent lighting and high frequency details Drew at all predicts one environment map for each query location but with no guarantee for spatial consistency Wong at all predicts a volu
