论文标题
多览神经表面重建，通过解开几何形状和外观
Multiview Neural Surface Reconstruction by Disentangling Geometry and Appearance
论文作者
论文摘要
在这项工作中，我们解决了多视图3D表面重建的具有挑战性的问题。我们介绍了一种神经网络架构，该架构同时学习了未知的几何形状，相机参数和神经渲染器，该几何形状，近似于从表面向相机反射到相机的光。几何形状表示为神经网络的零级集，而源自渲染方程的神经渲染器能够（隐含地）对广泛的照明条件和材料进行建模。我们在现实世界的2D图像上训练了我们的网络，具有不同的材料属性，照明条件和来自DTU MVS数据集的嘈杂摄像机初始化的对象图像。我们发现我们的模型以高保真，分辨率和细节生成最先进的3D表面重建状态。
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.
