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

2017

Conference Paper

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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. Code is available in https://github.com/mjiUST/SurfaceNet

Author(s): Ji, Mengqi and Gall, Juergen and Zheng, Haitian and Liu, Yebin and Fang, Lu
Book Title: IEEE International Conference on Computer Vision (ICCV), 2017
Year: 2017
Publisher: IEEE Computer Society

Department(s): Autonomous Vision
Bibtex Type: Conference Paper (inproceedings)
Paper Type: Conference

Event Name: 2017 IEEE International Conference on Computer Vision (ICCV)
Event Place: Venice, Italy

URL: https://github.com/mjiUST/SurfaceNet

BibTex

@inproceedings{ji2017surfacenet,
  title = {SurfaceNet: An End-to-End 3D Neural Network for Multiview Stereopsis},
  author = {Ji, Mengqi and Gall, Juergen and Zheng, Haitian and Liu, Yebin and Fang, Lu},
  booktitle = {IEEE International Conference on Computer Vision (ICCV), 2017},
  publisher = {IEEE Computer Society},
  year = {2017},
  url = {https://github.com/mjiUST/SurfaceNet}
}