The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.
Viewpoints and keypoints
Abstract
We characterize the problem of pose estimation for rigid objects in terms of determining viewpoint to explain coarse pose and keypoint prediction to capture the finer details. We address both these tasks in two different settings - the constrained setting with known bounding boxes and the more challenging detection setting where the aim is to simultaneously detect and correctly estimate pose of objects. We present Convolutional Neural Network based architectures for these and demonstrate that leveraging viewpoint estimates can substantially improve local appearance based keypoint predictions. In addition to achieving significant improvements over state-of-the-art in the above tasks, we analyze the error modes and effect of object characteristics on performance to guide future efforts towards this goal.
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Cites
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155
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Backpropagation Applied to Handwritten Zip Code Recognition
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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
The Pascal Visual Object Classes Challenge: A Retrospective
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DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition
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Cited by
PoseCNN: A Convolutional Neural Network for 6D Object Pose Estimation in Cluttered Scenes
Xiang, Y., Schmidt, T., Narayanan, V., & Fox, D. (2018) · Robotics: Science and Systems XIV · doi:10.15607/rss.2018.xiv.019 · Cited by 2,139
3D Bounding Box Estimation Using Deep Learning and Geometry
Mousavian, A., Anguelov, D., Flynn, J., & Kosecka, J. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.597 · Cited by 1,129
PointFusion: Deep Sensor Fusion for 3D Bounding Box Estimation
Xu, D., Anguelov, D., & Jain, A. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00033 · Cited by 856
Cross Modal Distillation for Supervision Transfer
Gupta, S., Hoffman, J., & Malik, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.309 · Cited by 530
“…include almost all state-of-the-art systems: object detection [12], semantic segmentation [28], object segmentation [17], and pose estimation [38], which start from generic features that are learned on the ImageNet dataset [6] using over a million labeled images and specialize them for each…”
BackgroundSelf-citationSegmentation-Driven 6D Object Pose Estimation
Hu, Y., Hugonot, J., Fua, P., & Salzmann, M. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00350 · Cited by 333
Metadata
- Authors
- Tulsiani, S., & Malik, J.
- Venue
- 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2015-06
- Pages
- 1510-1519
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W1949483711
- Cited by
- 346
- References
- 8
- FWCI
- 16.21
- Type
- conference paper
- Language
- en
- Open access
- closed
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