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.

本文介绍一种神经网络架构，通过联合学习未知几何形状、相机参数和神经渲染器，能够高质量、高精度、高细节地实现多角度 3D 表面重建。该方法通过零级集的神经网络表示几何形状，并采用基于渲染方程的神经渲染器来预测表面上的光的反射情况，具有广泛的光照和材质建模能力。