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.

中文翻译：

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通过解开几何和外观的多视图神经表面重建

在这项工作中，我们解决了多视图 3D 表面重建的挑战性问题。我们引入了一个神经网络架构，它同时学习未知的几何形状、相机参数，以及一个近似从表面反射到相机的光的神经渲染器。几何图形表示为神经网络的零水平集，而从渲染方程派生的神经渲染器能够（隐式）建模广泛的照明条件和材料。我们在具有不同材料属性、照明条件和来自 DTU MVS 数据集的嘈杂相机初始化的对象的真实世界 2D 图像上训练我们的网络。我们发现我们的模型可以产生具有高保真度、分辨率和细节的最先进的 3D 表面重建。