Journal
2017 IEEE INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV)
Volume -, Issue -, Pages 2326-2334Publisher
IEEE
DOI: 10.1109/ICCV.2017.253
Keywords
-
Funding
- Natural Science Foundation of China (NSFC) [61331015, 6152211, 61571259, 61531014]
- National key foundation for exploring scientific instrument [2013YQ140517]
- Shenzhen Fundamental Research fund [JCYJ20170307153051701]
- DFG as part of the DFG Research Unit Mapping on Demand (MoD) [GA 1927/2-2, FOR 1505]
Ask authors/readers for more resources
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.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available