The DOI matches its Crossref record; 32 of the 32 references it cites were found in a registry or an index.
Deep Learning of Local RGB-D Patches for 3D Object Detection and 6D Pose Estimation
Open accessAbstract
We present a 3D object detection method that uses regressed descriptors of locally-sampled RGB-D patches for 6D vote casting. For regression, we employ a convolutional auto-encoder that has been trained on a large collection of random local patches. During testing, scene patch descriptors are matched against a database of synthetic model view patches and cast 6D object votes which are subsequently filtered to refined hypotheses. We evaluate on three datasets to show that our method generalizes well to previously unseen input data, delivers robust detection results that compete with and surpass the state-of-the-art while being scalable in the number of objects.
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Cites
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Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation
Girshick, R., Donahue, J., Darrell, T., & Malik, J. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2014.81 · Cited by 32,684
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Stacked Convolutional Auto-Encoders for Hierarchical Feature Extraction
Masci, J., Meier, U., Cireşan, D., & Schmidhuber, J. (2011) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-21735-7_7 · Cited by 1,955
Learning Rich Features from RGB-D Images for Object Detection and Segmentation
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Self-citationScalable Nearest Neighbor Algorithms for High Dimensional Data
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Self-citationEnsemble of exemplar-SVMs for object detection and beyond
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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
Deep Hough Voting for 3D Object Detection in Point Clouds
Qi, C. R., Litany, O., He, K., & Guibas, L. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00937 · Cited by 1,244
Implicit 3D Orientation Learning for 6D Object Detection from RGB Images
Sundermeyer, M., Marton, Z.-C., Durner, M., Brucker, M., & Triebel, R. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-01231-1_43 · Cited by 564
Making Deep Heatmaps Robust to Partial Occlusions for 3D Object Pose Estimation
Oberweger, M., Rad, M., & Lepetit, V. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-01267-0_8 · Cited by 253
Deep learning in robotics: survey on model structures and training strategies
Karoly, A. I., Galambos, P., Kuti, J., & Rudas, I. J. (2021) · IEEE Transactions on Systems, Man, and Cybernetics: Systems · doi:10.1109/tsmc.2020.3018325 · Cited by 146
Metadata
- Authors
- Kehl, W., Milletari, F., Tombari, F., Ilic, S., & Navab, N.
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
- Published
- 2016
- Pages
- 205-220
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W2488101876
- Cited by
- 283
- References
- 32
- FWCI
- 13.63
- Type
- conference paper
- Language
- en
- Open access
- green
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- Kehl, W., Milletari, F., Tombari, F., Ilic, S., & Navab, N. (2016). Deep Learning of Local RGB-D Patches for 3D Object Detection and 6D Pose Estimation. Lecture Notes in Computer Science. https://doi.org/10.1007/978-3-319-46487-9_13
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