SurfaceNet: An End-To-End 3D Neural Network for Multiview Stereopsis

Mengqi Ji, Juergen Gall, Haitian Zheng, Yebin Liu, Lu Fang; The IEEE International Conference on Computer Vision (ICCV), 2017, pp. 2307-2315

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.

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[bibtex]
@InProceedings{Ji_2017_ICCV,
author = {Ji, Mengqi and Gall, Juergen and Zheng, Haitian and Liu, Yebin and Fang, Lu},
title = {SurfaceNet: An End-To-End 3D Neural Network for Multiview Stereopsis},
booktitle = {The IEEE International Conference on Computer Vision (ICCV)},
month = {Oct},
year = {2017}
}