The DOI matches its Crossref record; 10 of the 10 references it cites were found in a registry or an index.
Multi-view Convolutional Neural Networks for 3D Shape Recognition
Open accessAbstract
A longstanding question in computer vision concerns the representation of 3D shapes for recognition: should 3D shapes be represented with descriptors operating on their native 3D formats, such as voxel grid or polygon mesh, or can they be effectively represented with view-based descriptors? We address this question in the context of learning to recognize 3D shapes from a collection of their rendered views on 2D images. We first present a standard CNN architecture trained to recognize the shapes' rendered views independently of each other, and show that a 3D shape can be recognized even from a single view at an accuracy far higher than using state-of-the-art 3D shape descriptors. Recognition rates further increase when multiple views of the shapes are provided. In addition, we present a novel CNN architecture that combines information from multiple views of a 3D shape into a single and compact shape descriptor offering even better recognition performance. The same architecture can be applied to accurately recognize human hand-drawn sketches of shapes. We conclude that a collection of 2D views can be highly informative for 3D shape recognition and is amenable to emerging CNN architectures and their derivatives.
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Cites
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Metadata
- Authors
- Su, H., Maji, S., Kalogerakis, E., & Learned-Miller, E.
- Venue
- 2015 IEEE International Conference on Computer Vision (ICCV)
- Publisher
- IEEE
- Published
- 2015-12
- Pages
- 945-953
- OpenAlex
- W1644641054
- Cited by
- 3,480
- References
- 10
- FWCI
- 273.69
- Type
- conference paper
- Language
- en
- Open access
- green
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- Su, H., Maji, S., Kalogerakis, E., & Learned-Miller, E. (2015). Multi-view Convolutional Neural Networks for 3D Shape Recognition. 2015 IEEE International Conference on Computer Vision (ICCV). https://doi.org/10.1109/iccv.2015.114
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