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In this work we address the challenging problem of multiview 3D surfacereconstruction. We introduce a neural network architecture that simultaneouslylearns the unknown geometry, camera parameters, and a neural renderer thatapproximates the light reflected from the surface towards the camera. Thegeometry is represented as a zero level-set of a neural network, while theneural renderer, derived from the rendering equation, is capable of(implicitly) modeling a wide set of lighting conditions and materials. Wetrained our network on real world 2D images of objects with different materialproperties, lighting conditions, and noisy camera initializations from the DTUMVS dataset. We found our model to produce state of the art 3D surfacereconstructions with high fidelity, resolution and detail.

Multiview Neural Surface Reconstruction by Disentangling Geometry and Appearance