SurfaceNet: An End-to-End 3D Neural Network for Multiview Stereopsis
Top Cited Papers
- 1 October 2017
- conference paper
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- p. 2326-2334
- https://doi.org/10.1109/iccv.2017.253
Abstract
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.Keywords
This publication has 24 references indexed in Scilit:
- Large-Scale Data for Multiple-View StereopsisInternational Journal of Computer Vision, 2016
- Massively Parallel Multiview Stereopsis by Surface Normal DiffusionPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2015
- Holistically-Nested Edge DetectionPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2015
- VoxNet: A 3D Convolutional Neural Network for real-time object recognitionPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2015
- Motion estimation via hierarchical block matching and graph cutPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2015
- FaceNet: A unified embedding for face recognition and clusteringPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2015
- Computing the stereo matching cost with a convolutional neural networkPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2015
- A novel Ray-space based view generation algorithm via Radon transform3D Research, 2013
- PatchMatchACM Transactions on Graphics, 2009
- A Surface-Growing Approach to Multi-View Stereo ReconstructionPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2007