The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.
Detailed 3D Representations for Object Recognition and Modeling
Abstract
Geometric 3D reasoning at the level of objects has received renewed attention recently in the context of visual scene understanding. The level of geometric detail, however, is typically limited to qualitative representations or coarse boxes. This is linked to the fact that today's object class detectors are tuned toward robust 2D matching rather than accurate 3D geometry, encouraged by bounding-box-based benchmarks such as Pascal VOC. In this paper, we revisit ideas from the early days of computer vision, namely, detailed, 3D geometric object class representations for recognition. These representations can recover geometrically far more accurate object hypotheses than just bounding boxes, including continuous estimates of object pose and 3D wireframes with relative 3D positions of object parts. In combination with robust techniques for shape description and inference, we outperform state-of-the-art results in monocular 3D pose estimation. In a series of experiments, we analyze our approach in detail and demonstrate novel applications enabled by such an object class representation, such as fine-grained categorization of cars and bicycles, according to their 3D geometry, and ultrawide baseline matching.
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Cites
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Multiple View Geometry in Computer Vision
Hartley, R., & Zisserman, A. (2004) · doi:10.1017/cbo9780511811685 · Cited by 21,070
A decision-theoretic generalization of on-line learning and an application to boosting
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Object Detection with Discriminatively Trained Part-Based Models
Felzenszwalb, P. F., Girshick, R. B., McAllester, D., & Ramanan, D. (2010) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2009.167 · Cited by 9,853
Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories
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Automated Flower Classification over a Large Number of Classes
Nilsback, M.-E., & Zisserman, A. (2008) · 2008 Sixth Indian Conference on Computer Vision, Graphics & Image Processing · doi:10.1109/icvgip.2008.47 · Cited by 3,442
Cited by
VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection
Zhou, Y., & Tuzel, O. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00472 · Cited by 4,813
Multi-view 3D Object Detection Network for Autonomous Driving
Chen, X., Ma, H., Wan, J., Li, B., & Xia, T. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.691 · Cited by 3,450
3D-R2N2: A Unified Approach for Single and Multi-view 3D Object Reconstruction
Choy, C. B., Xu, D., Gwak, J., Chen, K., & Savarese, S. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46484-8_38 · Cited by 1,632
Mvx-net: Multimodal voxelnet for 3d object detection
Sindagi, V. A., Zhou, Y., & Tuzel, O. (2019) · 2019 International Conference on Robotics and Automation (ICRA) · doi:10.1109/icra.2019.8794195 · Cited by 554
Seeing 3D Chairs: Exemplar Part-Based 2D-3D Alignment Using a Large Dataset of CAD Models
Aubry, M., Maturana, D., Efros, A. A., Russell, B. C., & Sivic, J. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2014.487 · Cited by 473
Metadata
- Authors
- Zia, M. Z., Stark, M., Schiele, B., & Schindler, K.
- Venue
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Published
- 2013-11
- Volume
- 35
- Issue
- 11
- Pages
- 2608-2623
- ISSN
- 0162-8828 (ISSN-L)
- 2160-9292
- OpenAlex
- W2114111978
- Cited by
- 165
- References
- 7
- FWCI
- 25.28
- Type
- journal article
- Language
- en
- Open access
- closed
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- Zia, M. Z., Stark, M., Schiele, B., & Schindler, K. (2013). Detailed 3D Representations for Object Recognition and Modeling. IEEE Transactions on Pattern Analysis and Machine Intelligence. https://doi.org/10.1109/tpami.2013.87
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