The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.
Learning Category-Specific Mesh Reconstruction from Image Collections
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Cites
The Unreasonable Effectiveness of Deep Features as a Perceptual Metric
Zhang, R., Isola, P., Efros, A. A., Shechtman, E., & Wang, O. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00068 · Cited by 14,623
Self-citationIdentity mappings in deep residual networks
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46493-0_38 · Cited by 10,741
Identity Mappings in Deep Residual Networks
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · arXiv · doi:10.48550/arxiv.1603.05027 · Cited by 10,665
Loper, M., Mahmood, N., Romero, J., Pons-Moll, G., & J, B. M. (2015) · ACM Transactions on Graphics · doi:10.1145/2816795.2818013 · Cited by 3,734
He, K., Gkioxari, G., Dollar, P., & Girshick, R. (2020) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2018.2844175 · Cited by 3,457
SCAPE: shape completion and animation of people
Anguelov, D., Srinivasan, P., Koller, D., Thrun, S., Rodgers, J., & Davis, J. (2005) · ACM Transactions on Graphics · doi:10.1145/1073204.1073207 · Cited by 1,489
He, K., Gkioxari, G., Dollár, P., & Girshick, R. (2017) · arXiv · doi:10.48550/arxiv.1703.06870 · Cited by 406
Cited by
pi-GAN: Periodic Implicit Generative Adversarial Networks for 3D-Aware Image Synthesis
Chan, E. R., Monteiro, M., Kellnhofer, P., Wu, J., & Wetzstein, G. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.00574 · Cited by 689
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
HoloGAN: Unsupervised Learning of 3D Representations From Natural Images
Nguyen-Phuoc, T., Li, C., Theis, L., Richardt, C., & Yang, Y. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00768 · Cited by 340
Escaping Plato’s Cave: 3D Shape From Adversarial Rendering
Henzler, P., Mitra, N., & Ritschel, T. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.01008 · Cited by 194
Three-D Safari: Learning to Estimate Zebra Pose, Shape, and Texture From Images “In the Wild”
Zuffi, S., Kanazawa, A., Berger-Wolf, T., & Black, M. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00546 · Cited by 139
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Metadata
- Authors
- Kanazawa, A., Tulsiani, S., Efros, A. A., & Malik, J.
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
- Published
- 2018
- Pages
- 386-402
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W2963850211
- Cited by
- 530
- References
- 7
- FWCI
- 91.29
- Type
- conference paper
- Language
- en
- Open access
- green
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- CoChat. (2026). Citation check: Learning Category-Specific Mesh Reconstruction from Image Collections. https://app.cochat.ai/papers/10.1007/978-3-030-01267-0_23
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- Kanazawa, A., Tulsiani, S., Efros, A. A., & Malik, J. (2018). Learning Category-Specific Mesh Reconstruction from Image Collections. Lecture Notes in Computer Science. https://doi.org/10.1007/978-3-030-01267-0_23
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
