SurfaceNet: An End-to-End 3D Neural Network for Multiview Stereopsis 论文
2017引用 353
Advanced Vision and ImagingComputer Graphics and Visualization Techniques3D Shape Modeling and Analysis
详细信息
- 发表日期
- 2017-10-01
- 发表年份
- 2017
关键词
Advanced Vision and ImagingComputer Graphics and Visualization Techniques3D Shape Modeling and Analysis
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.