Multiview Neural Surface Reconstruction by Disentangling Geometry and Appearance

• URL: http://arxiv.org/abs/2003.09852v3
• Date: Sun, 25 Oct 2020 10:30:06 GMT
• Title: Multiview Neural Surface Reconstruction by Disentangling Geometry and Appearance
• Authors: Lior Yariv, Yoni Kasten, Dror Moran, Meirav Galun, Matan Atzmon, Ronen Basri, Yaron Lipman
• Abstract summary: We introduce a neural network that simultaneously learns the unknown geometry, camera parameters, and a neural architecture that approximates the light reflected from the surface towards the camera. We trained our network on real world 2D images of objects with different material properties, lighting conditions, and noisy camera materials from the DTU MVS dataset.
• Score: 46.488713939892136
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this work we address the challenging problem of multiview 3D surface reconstruction. We introduce a neural network architecture that simultaneously learns the unknown geometry, camera parameters, and a neural renderer that approximates the light reflected from the surface towards the camera. The geometry is represented as a zero level-set of a neural network, while the neural renderer, derived from the rendering equation, is capable of (implicitly) modeling a wide set of lighting conditions and materials. We trained our network on real world 2D images of objects with different material properties, lighting conditions, and noisy camera initializations from the DTU MVS dataset. We found our model to produce state of the art 3D surface reconstructions with high fidelity, resolution and detail.

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