@inproceedings{d3fcd51c3ab043719f0cfff491c0d1de,
title = "SurfaceNet: An End-to-End 3D Neural Network for Multiview Stereopsis",
abstract = "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.",
author = "Mengqi Ji and Juergen Gall and Haitian Zheng and Yebin Liu and Lu Fang",
note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 16th IEEE International Conference on Computer Vision, ICCV 2017 ; Conference date: 22-10-2017 Through 29-10-2017",
year = "2017",
month = dec,
day = "22",
doi = "10.1109/ICCV.2017.253",
language = "英语",
series = "Proceedings of the IEEE International Conference on Computer Vision",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "2326--2334",
booktitle = "Proceedings - 2017 IEEE International Conference on Computer Vision, ICCV 2017",
address = "美国",
}