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Traditional computer graphics rendering pipeline is designed for procedurallygenerating 2D quality images from 3D shapes with high performance. Thenon-differentiability due to discrete operations such as visibility computationmakes it hard to explicitly correlate rendering parameters and the resultingimage, posing a significant challenge for inverse rendering tasks. Recent workon differentiable rendering achieves differentiability either by designingsurrogate gradients for non-differentiable operations or via an approximate butdifferentiable renderer. These methods, however, are still limited when itcomes to handling occlusion, and restricted to particular rendering effects. Wepresent RenderNet, a differentiable rendering convolutional network with anovel projection unit that can render 2D images from 3D shapes. Spatialocclusion and shading calculation are automatically encoded in the network. Ourexperiments show that RenderNet can successfully learn to implement differentshaders, and can be used in inverse rendering tasks to estimate shape, pose,lighting and texture from a single image.

RenderNet: A deep convolutional network for differentiable rendering from 3D shapes