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

Mengqi Ji, Juergen Gall, Haitian Zheng, Yebin Liu, Lu Fang.
IEEE International Conference on Computer Vision. 2017 Aug 5.

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Introduction

SurfaceNet is an end-to-end learning framework for multiview stereopsis. 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 as 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.

Framework Framework

Citation

If you find this project useful for your research, please cite:

@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={Proceedings of the IEEE International Conference on Computer Vision (ICCV)},
  pages={2307--2315},
  year={2017}
}