The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Large-Scale Data for Multiple-View Stereopsis
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Cites
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
DTAM: Dense tracking and mapping in real-time
Newcombe, R. A., Lovegrove, S. J., & Davison, A. J. (2011) · 2011 International Conference on Computer Vision · doi:10.1109/iccv.2011.6126513 · Cited by 2,078 · cited 1×
“…meth-ods convert photo-consistency data into a 3D point-cloud, which is then converted into a 3D triangulated surface using standard meshing techniques such as Poisson reconstruction (Kazhdan et al. 2006), graph cuts (Vogiatzis et al. 2007) or signed distance functions (Newcombe et al. 2011).”
MethodHigh-accuracy stereo depth maps using structured light
Scharstein, D., & Szeliski, R. (2003) · 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings. · doi:10.1109/cvpr.2003.1211354 · Cited by 1,542
Cited by
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
Image Matching from Handcrafted to Deep Features: A Survey
Ma, J., Jiang, X., Fan, A., Jiang, J., & Yan, J. (2021) · International Journal of Computer Vision · doi:10.1007/s11263-020-01359-2 · Cited by 990
Differentiable Volumetric Rendering: Learning Implicit 3D Representations Without 3D Supervision
Niemeyer, M., Mescheder, L., Oechsle, M., & Geiger, A. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00356 · Cited by 834
Recurrent MVSNet for High-Resolution Multi-View Stereo Depth Inference
Yao, Y., Luo, Z., Li, S., Shen, T., Fang, T., & Quan, L. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00567 · Cited by 666
UNISURF: Unifying Neural Implicit Surfaces and Radiance Fields for Multi-View Reconstruction
Oechsle, M., Peng, S., & Geiger, A. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00554 · Cited by 622
Metadata
- Authors
- Aanæs, H., Jensen, R. R., Vogiatzis, G., Tola, E., & Dahl, A. B.
- Venue
- International Journal of Computer Vision
- Published
- 2016-04-23
- Volume
- 120
- Issue
- 2
- Pages
- 153-168
- ISSN
- 0920-5691 (ISSN-L)
- 1573-1405
- OpenAlex
- W2338968644
- Cited by
- 672
- References
- 3
- FWCI
- 10.72
- Type
- journal article
- Language
- en
- Open access
- green
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- Aanæs, H., Jensen, R. R., Vogiatzis, G., Tola, E., & Dahl, A. B. (2016). Large-Scale Data for Multiple-View Stereopsis. International Journal of Computer Vision. https://doi.org/10.1007/s11263-016-0902-9
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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