The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.
Joint embeddings of shapes and images via CNN image purification
Open accessAbstract
Both 3D models and 2D images contain a wealth of information about everyday objects in our environment. However, it is difficult to semantically link together these two media forms, even when they feature identical or very similar objects. We propose a joint embedding space populated by both 3D shapes and 2D images of objects, where the distances between embedded entities reflect similarity between the underlying objects. This joint embedding space facilitates comparison between entities of either form, and allows for cross-modality retrieval. We construct the embedding space using 3D shape similarity measure, as 3D shapes are more pure and complete than their appearance in images, leading to more robust distance metrics. We then employ a Convolutional Neural Network (CNN) to "purify" images by muting distracting factors. The CNN is trained to map an image to a point in the embedding space, so that it is close to a point attributed to a 3D model of a similar object to the one depicted in the image. This purifying capability of the CNN is accomplished with the help of a large amount of training data consisting of images synthesized from 3D shapes. Our joint embedding allows cross-view image retrieval, image-based shape retrieval, as well as shape-based image retrieval. We evaluate our method on these retrieval tasks and show that it consistently out-performs state-of-the-art methods, and demonstrate the usability of a joint embedding in a number of additional applications.
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Cites
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BackgroundMethodImage Classification with the Fisher Vector: Theory and Practice
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Deep Metric Learning via Lifted Structured Feature Embedding
Song, H. O., Xiang, Y., Jegelka, S., & Savarese, S. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.434 · Cited by 1,630
A Papier-Mache Approach to Learning 3D Surface Generation
Groueix, T., Fisher, M., Kim, V. G., Russell, B. C., & Aubry, M. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00030 · Cited by 910
The sketchy database: learning to retrieve badly drawn bunnies
Sangkloy, P., Burnell, N., Ham, C., & Hays, J. (2016) · ACM Transactions on Graphics · doi:10.1145/2897824.2925954 · Cited by 622
Learning Shape Templates With Structured Implicit Functions
Genova, K., Cole, F., Vlasic, D., Sarna, A., Freeman, W., & Funkhouser, T. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00725 · Cited by 323
3D Face Reconstruction by Learning from Synthetic Data
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Metadata
- Authors
- Li, Y., Su, H., Qi, C. R., Fish, N., Cohen-Or, D., & Guibas, L. J.
- Venue
- ACM Transactions on Graphics
- Published
- 2015-11-02
- Volume
- 34
- Issue
- 6
- Pages
- 1-12
- ISSN
- 0730-0301 (ISSN-L)
- 1557-7368
- OpenAlex
- W2083163329
- Cited by
- 174
- References
- 7
- FWCI
- 21.02
- Type
- journal article
- Language
- en
- Open access
- bronze
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- Li, Y., Su, H., Qi, C. R., Fish, N., Cohen-Or, D., & Guibas, L. J. (2015). Joint embeddings of shapes and images via CNN image purification. ACM Transactions on Graphics. https://doi.org/10.1145/2816795.2818071
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