3D-SSD: Learning hierarchical features from RGB-D images for amodal 3D object detection
Open accessIntegrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Experimental results show that using depth information, optical flow information, and LiDAR point clouds in addition to conventional RGB data can improve the performance of one-stage based detection systems.
“According to experimental results, it has been shown that the use of depth information [183, 184], optical flow information [185], and LiDAR point clouds [186] in addition to conventional RGB data can improve the performance of one-stage based detection systems.”
From A survey on deep multimodal learning for computer vision: advances, trends, applications, and datasets (2022) · cites this paper as [184]
Cites
You Only Look Once: Unified, Real-Time Object Detection
Redmon, J., Divvala, S., Girshick, R., & Farhadi, A. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.91 · Cited by 53,803 · see citing works
Microsoft COCO: Common Objects in Context
Lin, T.-Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., & Zitnick, C. L. (2014) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-10602-1_48 · Cited by 45,460 · see citing works
ImageNet Large Scale Visual Recognition Challenge
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al. (2015) · International Journal of Computer Vision · doi:10.1007/s11263-015-0816-y · Cited by 39,380 · see citing works
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 · see citing works
(2016) · Cited by 31,590 · see citing works
Feature Pyramid Networks for Object Detection
Lin, T.-Y., Dollar, P., Girshick, R., He, K., Hariharan, B., & Belongie, S. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.106 · Cited by 30,939 · see citing works
Fast R-CNNPreprint
Girshick, R. B. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1504.08083 · Cited by 29,416 · see citing works
The pascal visual object classes (VOC) challenge
Everingham, M., Van Gool, L., Williams, C. K. I., Winn, J., & Zisserman, A. (2010) · International Journal of Computer Vision · doi:10.1007/s11263-009-0275-4 · Cited by 19,599 · see citing works
Proceedings of IEEE Conference on Computer Vision and Pattern Recognition
(1993) · doi:10.1109/cvpr.1993 · Cited by 17,989 · see citing works
(2010) · Cited by 14,134 · see citing works
Proceedings of IEEE International Conference on Computer Vision
(1995) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.1995.466933 · Cited by 13,117 · see citing works
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 · see citing works
Indoor segmentation and support inference from rgbd images
Silberman, N., Hoiem, D., Kohli, P., & Fergus, R. (2012) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-33715-4_54 · Cited by 5,760 · see citing works
Proceedings European Conference on Computer Vision 2002
Heyden, A., Sparr, G., Nielsen, M., & Johansen, P. (2002) · Lund University Publications (Lund University) · Cited by 5,153 · see citing works
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 · see citing works
Self-citationSUN RGB-D: A RGB-D scene understanding benchmark suite
Song, S., Lichtenberg, S. P., & Xiao, J. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298655 · Cited by 1,943 · see citing works
Fast Feature Pyramids for Object Detection
Dollár, P., Appel, R., Belongie, S., & Perona, P. (2014) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2014.2300479 · Cited by 1,941 · see citing works
A Unified Multi-scale Deep Convolutional Neural Network for Fast Object Detection
Cai, Z., Fan, Q., Feris, R. S., & Vasconcelos, N. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46493-0_22 · Cited by 1,605 · see citing works
Learning Rich Features from RGB-D Images for Object Detection and Segmentation
Gupta, S., Girshick, R., Arbeláez, P., & Malik, J. (2014) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-10584-0_23 · Cited by 1,528 · see citing works
Mask R-CNNPreprint
He, K., Gkioxari, G., Dollár, P., & Girshick, R. (2017) · arXiv (Cornell University) · Cited by 1,252 · see citing works
Focal Loss for Dense Object Detection
Lin, T.-Y., Goyal, P., Girshick, R., He, K., & Dollár, P. (2017) · arXiv · doi:10.48550/arxiv.1708.02002 · Cited by 1,227 · see citing works
Multiscale Combinatorial Grouping
Arbelaez, P., Pont-Tuset, J., Barron, J., Marques, F., & Malik, J. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2014.49 · Cited by 1,155 · see citing works
Deep Sliding Shapes for Amodal 3D Object Detection in RGB-D Images
Song, S., & Xiao, J. (2016) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2016.94 · Cited by 671 · see citing works
GVCNN: Group-View Convolutional Neural Networks for 3D Shape Recognition
Feng, Y., Zhang, Z., Zhao, X., et al. (2018) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2018.00035 · Cited by 652 · see citing works
CPMC: Automatic Object Segmentation Using Constrained Parametric Min-Cuts
Carreira, J., & Sminchisescu, C. (2011) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2011.231 · Cited by 588 · see citing works
Sliding Shapes for 3D Object Detection in Depth Images
Song, S., & Xiao, J. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-10599-4_41 · Cited by 361 · see citing works
Holistic Scene Understanding for 3D Object Detection with RGBD Cameras
Lin, D., Fidler, S., & Urtasun, R. (2013) · 2013 IEEE International Conference on Computer Vision · doi:10.1109/iccv.2013.179 · Cited by 253 · see citing works
Aligning 3D models to RGB-D images of cluttered scenes
Gupta, S., Arbeláez, P., Girshick, R., & Malik, J. (2015) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2015.7299105 · Cited by 199 · see citing works
2D-Driven 3D Object Detection in RGB-D Images
Lahoud, J., & Ghanem, B. (2017) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.495 · Cited by 177 · see citing works
Three-Dimensional Object Detection and Layout Prediction Using Clouds of Oriented Gradients
Ren, Z., & Sudderth, E. B. (2016) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2016.169 · Cited by 138 · see citing works
Amodal Detection of 3D Objects: Inferring 3D Bounding Boxes from 2D Ones in RGB-Depth Images
Deng, Z., & Latecki, L. J. (2017) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2017.50 · Cited by 99 · see citing works
Lei, J., Duan, J., Wu, F., et al. (2016) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2016.2617332 · Cited by 94 · see citing works
DeepContext: Context-Encoding Neural Pathways for 3D Holistic Scene Understanding
Zhang, Y., Bai, M., Kohli, P., Izadi, S., & Xiao, J. (2017) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.135 · Cited by 69 · see citing works
Accurate Localization of 3D Objects from RGB-D Data Using Segmentation Hypotheses
Kim, B.-S., Xu, S., & Savarese, S. (2013) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2013.409 · Cited by 60 · see citing works
A 3D model recognition mechanism based on deep Boltzmann machines
Leng, B., Zhang, X., Ming, Y., & Xiong, Z. (2014) · Neurocomputing · doi:10.1016/j.neucom.2014.06.084 · Cited by 59 · see citing works
Towards 3D object detection with bimodal deep Boltzmann machines over RGBD imagery
Liu, W., Ji, R., & Li, S. (2015) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2015.7298920 · Cited by 30 · see citing works
Maximum Cohesive Grid of Superpixels for Fast Object Localization
Li, L., Feng, W., Wan, L., et al. (2013) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2013.408 · Cited by 29 · see citing works
Shape-Preserving Object Depth Control for Stereoscopic Images
Lei, J., Peng, B., Zhang, C., et al. (2017) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2017.2749146 · Cited by 24 · see citing works
Effective 3D object detection based on detector and tracker
Nie, W., Liu, A.-A., Wang, Z., et al. (2016) · Neurocomputing · doi:10.1016/j.neucom.2015.06.118 · Cited by 12 · see citing works
Towards open-set semantic labeling in 3D point clouds : Analysis on the unknown class
Ma, H., Xiong, R., Wang, Y., Kodagoda, S., & Shi, L. (2017) · Neurocomputing · doi:10.1016/j.neucom.2017.09.071 · Cited by 11 · see citing works
Self-citation
Flagged first, then by citations
Cited by
Sorted by citations
Bayoudh, K., Knani, R., Hamdaoui, F., & Mtibaa, A. (2022) · The Visual Computer · doi:10.1007/s00371-021-02166-7 · Cited by 456 · see citing works
- Authors
- Luo, Q., Ma, H., Tang, L., Wang, Y., & Xiong, R.
- Affiliations
- State Key Laboratory of Industrial Control Technology (CN)
- Corresponding author
- Wang, Yue
- Xiong, Rong
- Venue
- Neurocomputing
- Publisher
- Elsevier BV
- Published
- 2020-02
- Volume
- 378
- Pages
- 364-374
- ISSN
- 0925-2312 (ISSN-L)
- OpenAlex
- W2980698950
- Cited by
- 69
- References
- 40
- Citations by year
- 2026: 4 · 2025: 7 · 2024: 12 · 2023: 11 · 2022: 10 · 2021: 14 · 2020: 7 · 2019: 3 · 2018: 1
- FWCI
- 3.81
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- Neurocomputing (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- National Key R&D Program of China (2017YFB1300400)
- State Key Laboratory of Industrial Control Technology (ITC1904)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Image and Video Retrieval Techniques
- Physical Sciences › Engineering › Computational Mechanics › 3D Shape Modeling and Analysis
- Keywords
- RGB-D, hierarchical feature fusion, multi-scale feature fusion, real-time detection, indoor object detection, SUN RGB-D, NYUv2
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
