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

被引：291

作者：

Ji, Mengqi ^{[1
]}

Gall, Juergen ^{[3
]}

Zheng, Haitian ^{[2
]}

Liu, Yebin ^{[2
]}

Fang, Lu ^{[2
]}

机构：

[1] Hong Kong Univ Sci & Technol, Hong Kong, Hong Kong, Peoples R China

[2] Tsinghua Univ, Beijing, Peoples R China

[3] Univ Bonn, Bonn, Germany

来源：

2017 IEEE INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV) | 2017年

关键词：

D O I：

10.1109/ICCV.2017.253

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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.

引用

页码：2326 / 2334

页数：9