The DOI matches its Crossref record; 25 of the 25 references it cites were found in a registry or an index.
SurfaceNet: An End-to-End 3D Neural Network for Multiview Stereopsis
Open accessAbstract
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.
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Cites
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
Ioffe, S., & Szegedy, C. (2015) · arXiv · doi:10.48550/arxiv.1502.03167 · Cited by 27,818
FaceNet: A unified embedding for face recognition and clustering
Schroff, F., Kalenichenko, D., & Philbin, J. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298682 · Cited by 15,267
Multi-scale context aggregation by dilated convolutions
Yu, F., & Koltun, V. (2015) · arXiv · doi:10.48550/arxiv.1511.07122 · Cited by 7,738
VoxNet: A 3D Convolutional Neural Network for real-time object recognition
Maturana, D., & Scherer, S. (2015) · 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2015.7353481 · Cited by 3,458
Holistically-Nested Edge Detection
Xie, S., & Tu, Z. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.164 · Cited by 3,096
Accurate, Dense, and Robust Multiview Stereopsis
Furukawa, Y., & Ponce, J. (2010) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2009.161 · Cited by 3,019
PatchMatch: a randomized correspondence algorithm for structural image editing
Barnes, C., Shechtman, E., Finkelstein, A., & Goldman, D. B. (2009) · ACM Transactions on Graphics · doi:10.1145/1531326.1531330 · Cited by 2,880
A Comparison and Evaluation of Multi-View Stereo Reconstruction Algorithms
Seitz, S. M., Curless, B., Diebel, J., Scharstein, D., & Szeliski, R. (2006) · 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1 (CVPR'06) · doi:10.1109/cvpr.2006.19 · Cited by 2,457
Volumetric and Multi-view CNNs for Object Classification on 3D Data
Qi, C. R., Su, H., NieBner, M., Dai, A., Yan, M., & Guibas, L. J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.609 · Cited by 1,504
Computing the stereo matching cost with a convolutional neural network
Zbontar, J., & LeCun, Y. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298767 · Cited by 796
Cited by
Occupancy Networks: Learning 3D Reconstruction in Function Space
Mescheder, L., Oechsle, M., Niemeyer, M., Nowozin, S., & Geiger, A. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00459 · Cited by 2,708
MVSNet: Depth Inference for Unstructured Multi-view Stereo
Yao, Y., Luo, Z., Li, S., Fang, T., & Quan, L. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-01237-3_47 · Cited by 1,455
BlendedMVS: A Large-Scale Dataset for Generalized Multi-View Stereo Networks
Yao, Y., Luo, Z., Li, S., Zhang, J., Ren, Y., Zhou, L., Fang, T., & Quan, L. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00186 · Cited by 478
PatchmatchNet: Learned Multi-View Patchmatch Stereo
Wang, F., Galliani, S., Vogel, C., Speciale, P., & Pollefeys, M. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.01397 · Cited by 420
Deep Stereo Using Adaptive Thin Volume Representation With Uncertainty Awareness
Cheng, S., Xu, Z., Zhu, S., Li, Z., Li, L. E., Ramamoorthi, R., & Su, H. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00260 · Cited by 385
Metadata
- Authors
- Ji, M., Gall, J., Zheng, H., Liu, Y., & Fang, L.
- Venue
- 2017 IEEE International Conference on Computer Vision (ICCV)
- Publisher
- IEEE
- Published
- 2017-10
- Pages
- 2326-2334
- OpenAlex
- W2745144057
- Cited by
- 361
- References
- 25
- FWCI
- 6.23
- Type
- conference paper
- Language
- en
- Open access
- green
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- CoChat. (2026). Citation check: SurfaceNet: An End-to-End 3D Neural Network for Multiview Stereopsis. https://app.cochat.ai/papers/10.1109/iccv.2017.253
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- Ji, M., Gall, J., Zheng, H., Liu, Y., & Fang, L. (2017). SurfaceNet: An End-to-End 3D Neural Network for Multiview Stereopsis. 2017 IEEE International Conference on Computer Vision (ICCV). https://doi.org/10.1109/iccv.2017.253
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