  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.
