The DOI matches its Crossref record; 11 of the 11 references it cites were found in a registry or an index.
3D-R2N2: A Unified Approach for Single and Multi-view 3D Object Reconstruction
Open accessAbstract
Inspired by the recent success of methods that employ shape priors to achieve robust 3D reconstructions, we propose a novel recurrent neural network architecture that we call the 3D Recurrent Reconstruction Neural Network (3D-R2N2). The network learns a mapping from images of objects to their underlying 3D shapes from a large collection of synthetic data [13]. Our network takes in one or more images of an object instance from arbitrary viewpoints and outputs a reconstruction of the object in the form of a 3D occupancy grid. Unlike most of the previous works, our network does not require any image annotations or object class labels for training or testing. Our extensive experimental analysis shows that our reconstruction framework (i) outperforms the state-of-the-art methods for single view reconstruction, and (ii) enables the 3D reconstruction of objects in situations when traditional SFM/SLAM methods fail (because of lack of texture and/or wide baseline).
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Cites
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Yu, X., Rao, Y., Wang, Z., Liu, Z., Lu, J., & Zhou, J. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.01227 · Cited by 544
Learning Joint Reconstruction of Hands and Manipulated Objects
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Metadata
- Authors
- Choy, C. B., Xu, D., Gwak, J., Chen, K., & Savarese, S.
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
- Published
- 2016
- Pages
- 628-644
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W2342277278
- Cited by
- 1,632
- References
- 11
- FWCI
- 237.91
- Type
- conference paper
- Language
- en
- Open access
- green
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- Choy, C. B., Xu, D., Gwak, J., Chen, K., & Savarese, S. (2016). 3D-R2N2: A Unified Approach for Single and Multi-view 3D Object Reconstruction. Lecture Notes in Computer Science. https://doi.org/10.1007/978-3-319-46484-8_38
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