SurfaceNet：一种端到端的三维多视立体神经网络
SurfaceNet: An End-to-end 3D Neural Network for Multiview Stereopsis
摘要：
本文提出了一个端到端的多视点立体视觉学习框架。我们称之为SurfaceNet网络。它将一组图像及其相应的相机参数作为输入，并直接推断3D模型。该框架的主要优点是，无论是照片的一致性，以及表面结构的几何关系，可以直接学习的目的，在一个端到端的方式的多视图立体视觉。SurfaceNet是一个完全3D卷积网络，它通过将相机参数与图像一起编码为3D体素表示来实现。我们在大规模DTU基准上评估SurfaceNet。
英文摘要：
This paper proposes an end-to-end learning framework for multiview stereopsis. We term the network SurfaceNet. It takes a set of images and their corresponding camera parameters as input and directly infers the 3D model. The key advantage of the framework is that both photo-consistency as well geometric relations of the surface structure can be directly learned for the purpose of multiview stereopsis in an end-to-end fashion. SurfaceNet is a fully 3D convolutional network which is achieved by encoding the camera parameters together with the images in a 3D voxel representation. We evaluate SurfaceNet on the large-scale DTU benchmark.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
