3D Pose Regression Using Convolutional Neural Networks
Open accessAbstract
3D pose estimation is a key component of many important computer vision tasks such as autonomous navigation and 3D scene understanding. Most state-of-the-art approaches to 3D pose estimation solve this problem as a pose-classification problem in which the pose space is discretized into bins and a CNN classifier is used to predict a pose bin. We argue that the 3D pose space is continuous and propose to solve the pose estimation problem in a CNN regression framework with a suitable representation, data augmentation and loss function that captures the geometry of the pose space. Experiments on PASCAL3D+ show that the proposed 3D pose regression approach achieves competitive performance compared to the state-of-the-art.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
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?
Geodesic loss L_geo is convex with respect to pose transformations.
“Geodesic loss, L geo , which is the square of the geodesic distance in SE(3), has been studied for registration problems due to its convexity with respect to pose transformations [32] [33].”
From A Fully Differentiable Framework for 2D/3D Registration and the Projective Spatial Transformers (2024) · cites this paper as [33]
In §3.4 (page unavailable), the paper says its geodesic-loss optimization encounters bad local minima and uses an initial MSE phase to avoid them, contrary to the claimed convexity. The paper discusses a loss on rotations in SO(3), not a squared geodesic distance in SE(3). (LLM, conf=0.77)
Checked by a reasoning model — may be wrong.
Experimental — accuracy not yet measuredL2 distances to quaternions near the equator do not express the geodesic distance.
“Quaternions, for example, are non-injective. While one can map quaternions to lie only on one hemisphere as in [38], L 2 distances to quaternions near the equator will still not express the geodesic distance.”
From Deep Learning for Spacecraft Pose Estimation from Photorealistic Rendering (2020) · cites this paper as [38]
Using adaptive weighting based on Laplace likelihood [38] achieved poor results.
“It is worth noting that we have experimented using the adaptive weighting based on Laplace likelihood in [38] but achieved poor results.”
From Deep Learning for Spacecraft Pose Estimation from Photorealistic Rendering (2020) · cites this paper as [38]
The citation identifies the purported adaptive-weighting method; the citing authors describe the poor results as their own. In §3.4 (page unavailable), the source describes a loss weighted by the number of images per category, but our copy does not identify it as adaptive weighting based on a Laplace likelihood. Searches for “adaptive weighting” and “Laplace likelihood” found no passage establishing that identification. (LLM, conf=0.75)
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
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 · see citing works
ImageNet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., & Fei-Fei, L. (2009) · 2009 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2009.5206848 · Cited by 65,333 · see citing works
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
Ren, S., He, K., Girshick, R., & Sun, J. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2577031 · Cited by 56,035 · see citing works
Return of the Devil in the Details: Delving Deep into Convolutional Nets
Chatfield, K., Simonyan, K., Vedaldi, A., & Zisserman, A. (2014) · Proceedings of the British Machine Vision Conference 2014 · doi:10.5244/c.28.6 · Cited by 3,335 · see citing works
XSEDE: Accelerating Scientific Discovery
Towns, J. W., Cockerill, T. M., Dahan, M., Foster, I. T., Gaither, K., Grimshaw, A. S., Hazlewood, V., Lathrop, S. A., Lifka, D. A., Peterson, G. D., et al. (2014) · Computing in Science & Engineering · doi:10.1109/mcse.2014.80 · Cited by 3,114 · see citing works
PoseNet: A Convolutional Network for Real-Time 6-DOF Camera Relocalization
Kendall, A., Grimes, M., & Cipolla, R. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.336 · Cited by 2,489 · see citing works
Geometric Loss Functions for Camera Pose Regression with Deep Learning
Kendall, A., & Cipolla, R. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.694 · Cited by 803 · see citing works
Beyond PASCAL: A benchmark for 3D object detection in the wild
Yu, X., Mottaghi, R., & Savarese, S. (2014) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv.2014.6836101 · Cited by 727 · see citing works
Render for CNN: Viewpoint Estimation in Images Using CNNs Trained with Rendered 3D Model Views
Su, H., Qi, C. R., Li, Y., & Guibas, L. J. (2015) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.308 · Cited by 680 · see citing works
Modelling uncertainty in deep learning for camera relocalization
Kendall, A., & Cipolla, R. (2016) · 2016 IEEE International Conference on Robotics and Automation (ICRA) · doi:10.1109/icra.2016.7487679 · Cited by 548 · cited 1× · see citing works
“Quaternions have also been used to represent 3D pose for camera localization in [11, 10, 9], but these works ignore the unit-norm constraint for computational ease and use a mean-squared or reprojection loss, whereas we incorporate the constraint into the network and use a geodesic loss.”
MethodTulsiani, S., & 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.2015.7298758 · Cited by 348 · see citing works
A statistical approach to 3d object detection applied to faces and cars
Schneiderman, H., & Takeo Kanade, A. (2000) · Cited by 243 · see citing works
Single Image 3D Interpreter Network
Tian, Y., Wu, J., Xue, T., Lim, J. J., Tenenbaum, J. B., Torralba, A. B., & Freeman, W. T. (2016) · DSpace@MIT (Massachusetts Institute of Technology) · Cited by 241 · see citing works
Teaching 3D geometry to deformable part models
Pepik, B., Stark, M. M., Gehler, P., & Schiele, B. (2012) · 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.2012.6248075 · Cited by 209 · see citing works
Nystrom, N. A., Levine, M. J., Roskies, R. Z., & Scott, J. R. (2015) · XSEDE Conference (XSEDE) · doi:10.1145/2792745.2792775 · Cited by 195 · see citing works
Distributed image-based 3-D localization of camera sensor networks
Tron, R., & Vidal, R. (2009) · IEEE Conference on Decision and Control (CDC) · doi:10.1109/cdc.2009.5400405 · Cited by 100 · see citing works
Self-citationGeometric Loss Functions for Camera Pose Regression with Deep LearningPreprint
Kendall, A. G., & Cipolla, R. (2017) · arXiv (Cornell University) · doi:10.17863/cam.26496 · Cited by 84 · see citing works
Analysis by Synthesis: 3D Object Recognition by Object Reconstruction
Hejrati, M., & Ramanan, D. (2014) · 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.2014.314 · Cited by 52 · see citing works
Elhoseiny, M., El-Gaaly, T., Bakry, A., & Elgammal, A. (2016) · International Conference on Machine Learning · Cited by 31 · see citing works
6-DoF Object Pose from Semantic Keypoints
Pavlakos, G., Zhou, X., Chan, A., Derpanis, K. G., & Daniilidis, K. (2017) · arXiv · doi:10.48550/arxiv.1703.04670 · Cited by 26 · see citing works
Digging Deep into the layers of CNNs: In Search of How CNNs Achieve View InvariancePreprint
Bakry, A., Elhoseiny, M., El-Gaaly, T., & Elgammal, A. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1508.01983 · Cited by 18 · see citing works
Flagged first, then by citations
3 of the 24 references couldn’t be matched to a known paper.
Research works printed but not linked: 1
- N. A. Nystrom, M. J. Levine, R. Z. Roskies, and J. R. Scott. Bridges: A uniquely flexible hpc resource for new communities and data analytics. In Proceed- ings of the 2015 XSEDE Conference: Scientific Ad- vancements Enabled by Enhanced Cyberinfrastruc- ture, XSEDE '15, pages 30:1-30:8, New York, NY, USA, 2015. ACM.
Cited by
Sorted by citations
Real-Time Seamless Single Shot 6D Object Pose Prediction
Tekin, B., Sinha, S. N., & Fua, P. (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.2018.00038 · Cited by 898 · see citing works
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 · see citing works
Application of deep learning algorithms in geotechnical engineering: a short critical review
Zhang, W., Li, H., Li, Y., Liu, H., Chen, Y., & Ding, X. (2021) · Artificial Intelligence Review · doi:10.1007/s10462-021-09967-1 · Cited by 548 · see citing works
Li, Z., Wang, G., & Ji, X. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00777 · Cited by 485 · see citing works
Satellite Pose Estimation Challenge: Dataset, Competition Design, and Results
Kisantal, M., Sharma, S., Park, T. H., Izzo, D., Martens, M., & D'Amico, S. (2020) · IEEE Transactions on Aerospace and Electronic Systems · doi:10.1109/taes.2020.2989063 · Cited by 304 · see citing works
Spacecraft pose estimation dataset (speed)
Sharma, S., Park, T. H., & D'Amico, S. (2026) · Stanford Digital Repository · doi:10.25740/dz692fn7184 · Cited by 304
RelocNet: Continuous Metric Learning Relocalisation Using Neural Nets
Balntas, V., Li, S., & Prisacariu, V. A. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01264-9_46 · Cited by 231 · see citing works
Deep Learning for Spacecraft Pose Estimation from Photorealistic Rendering
Proenca, P. F., & Gao, Y. (2020) · 2020 IEEE International Conference on Robotics and Automation (ICRA) · doi:10.1109/icra40945.2020.9197244 · Cited by 229 · see citing works
“[21] also regress the angle-axis vector, but they minimize directly the geodesic loss.”
BackgroundMethodNeural Network-Based Pose Estimation for Noncooperative Spacecraft Rendezvous
Sharma, S., & D'Amico, S. (2020) · IEEE Transactions on Aerospace and Electronic Systems · doi:10.1109/taes.2020.2999148 · Cited by 196 · see citing works
Augmented Autoencoders: Implicit 3D Orientation Learning for 6D Object Detection
Sundermeyer, M., Márton, Z.-C., Durner, M., & Triebel, R. (2019) · International Journal of Computer Vision · doi:10.1007/s11263-019-01243-8 · Cited by 148 · see citing works
Three-D Safari: Learning to Estimate Zebra Pose, Shape, and Texture From Images “In the Wild”
Zuffi, S., Kanazawa, A., Berger-Wolf, T., & Black, M. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00546 · Cited by 139 · see citing works
Real-Time Deep Pose Estimation With Geodesic Loss for Image-to-Template Rigid Registration
Salehi, S. S. M., Khan, S., Deniz Erdogmus, B., & Gholipour, A. (2018) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2018.2866442 · Cited by 133 · see citing works
A Vector-based Representation to Enhance Head Pose Estimation
Cao, Z., Chu, Z., Liu, D., & Chen, Y. (2021) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv48630.2021.00123 · Cited by 125 · see citing works
Real-time Deep Pose Estimation with Geodesic Loss for Image-to-Template Rigid Registration
Salehi, S. S. M., Khan, S., Erdogmus, D., & Gholipour, A. (2018) · arXiv · doi:10.48550/arxiv.1803.05982 · Cited by 118 · see citing works
Multi-Path Learning for Object Pose Estimation Across Domains
Sundermeyer, M., Durner, M., Puang, E. Y., Márton, Z.-C., Vaškevičius, N., Arras, K. O., & Triebel, R. (2020) · 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/cvpr42600.2020.01393 · Cited by 113 · see citing works
Image-based velocity estimation of rock using Convolutional Neural Networks
Karimpouli, S., & Tahmasebi, P. (2019) · Neural Networks · doi:10.1016/j.neunet.2018.12.006 · Cited by 100 · see citing works
Bukschat, Y., & Vetter, M. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2011.04307 · Cited by 99 · see citing works
6D Object Pose Regression via Supervised Learning on Point Clouds
Gao, G., Lauri, M., Wang, Y., Hu, X., Zhang, J., & Frintrop, S. (2020) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra40945.2020.9197461 · Cited by 95 · see citing works
Phisannupawong, T., Kamsing, P., Torteeka, P., Channumsin, S., Sawangwit, U., Hematulin, W., Jarawan, T., Somjit, T., Yooyen, S., Delahaye, D., et al. (2020) · Aerospace · doi:10.3390/aerospace7090126 · Cited by 84 · see citing works
A Geometric Approach to Obtain a Bird's Eye View From an Image
Abbas, S. A., & Zisserman, A. (2019) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw.2019.00504 · Cited by 71 · see citing works
Towards Robust Learning-Based Pose Estimation of Noncooperative Spacecraft
Park, T. H., Sharma, S., & D'Amico, S. (2019) · arXiv · doi:10.48550/arxiv.1909.00392 · Cited by 65 · see citing works
6D object position estimation from 2D images: a literature review
Marullo, G., Tanzi, L., Piazzolla, P., & Vezzetti, E. (2023) · Multimedia Tools and Applications · doi:10.1007/s11042-022-14213-z · Cited by 64 · see citing works
Optimizing Through Learned Errors for Accurate Sports Field Registration
Jiang, W., Higuera, J. C. G., Angles, B., Sun, W., Javan, M., & Yi, K. M. (2020) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv45572.2020.9093581 · Cited by 63 · see citing works
Three dimensional objects recognition & pattern recognition technique; related challenges: A review
Rani, S., Lakhwani, K., & Kumar, S. (2022) · Multimedia Tools and Applications · doi:10.1007/s11042-022-12412-2 · Cited by 62 · see citing works
Liebmann, F., von Atzigen, M., Stütz, D., Wolf, J., Zingg, L., Suter, D., Cavalcanti, N., Leoty, L., Esfandiari, H., Snedeker, J. G., et al. (2023) · Medical Image Analysis · doi:10.1016/j.media.2023.103027 · Cited by 58 · see citing works
An Analysis of SVD for Deep Rotation EstimationPreprint
Levinson, J., Esteves, C., Chen, K., Snavely, N., Kanazawa, A., Rostamizadeh, A., & Makadia, A. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2006.14616 · Cited by 56 · see citing works
Spherical Regression: Learning Viewpoints, Surface Normals and 3D Rotations on N-Spheres
Liao, S., Gavves, E., & Snoek, C. G. M. (2019) · 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.2019.00999 · Cited by 55 · see citing works
Convolutional Neural Network-Based Visual Servoing for Eye-to-Hand Manipulator
Tokuda, F., Arai, S., & Kosuge, K. (2021) · IEEE Access · doi:10.1109/access.2021.3091737 · Cited by 54 · see citing works
FADN: Fully Connected Attitude Detection Network Based on Industrial Video
Yang, J., Xi, M., Jiang, B., Man, J., Meng, Q., & Li, B. (2020) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2020.2984370 · Cited by 51 · see citing works
Deep Bingham Networks: Dealing with Uncertainty and Ambiguity in Pose Estimation
Deng, H., Bui, M., Navab, N., Guibas, L. J., Ilić, S., & Birdal, T. (2022) · International Journal of Computer Vision · doi:10.1007/s11263-022-01612-w · Cited by 50 · see citing works
Multi-person 3D Pose Estimation in Crowded Scenes Based on Multi-view Geometry
Chen, H., Guo, P., Li, P., Lee, G. H., & Chirikjian, G. S. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58580-8_32 · Cited by 48 · see citing works
Deep Predictive Motion Tracking in Magnetic Resonance Imaging: Application to Fetal Imaging
Singh, A., Salehi, S. S. M., & Gholipour, A. (2020) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2020.2998600 · Cited by 47 · see citing works
Lei, H., Liu, S., Elazab, A., Gong, X., & Lei, B. (2021) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2020.3027566 · Cited by 46 · see citing works
Generalizing Spatial Transformers to Projective Geometry with Applications to 2D/3D Registration
Gao, C., Liu, X., Gu, W., Killeen, B., Armand, M., Taylor, R., & Unberath, M. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-59716-0_32 · Cited by 46 · see citing works
Single Shot 6D Object Pose Estimation
Kleeberger, K., & Huber, M. F. (2020) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra40945.2020.9197207 · Cited by 45 · see citing works
Wide-Baseline Relative Camera Pose Estimation with Directional Learning
Chen, K., Snavely, N., & Makadia, A. (2021) · 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/cvpr46437.2021.00327 · Cited by 45 · see citing works
Deep Learning for Spacecraft Pose Estimation from Photorealistic Rendering
Proenca, P. F., & Gao, Y. (2019) · arXiv · Cited by 251 · see citing works
Drone Detection and Pose Estimation Using Relational Graph Networks
Jin, R., Jiang, J., Qi, Y., Lin, D., & Song, T. (2019) · Sensors · doi:10.3390/s19061479 · Cited by 38 · see citing works
A Fully Differentiable Framework for 2D/3D Registration and the Projective Spatial Transformers
Gao, C., Feng, A., Liu, X., Taylor, R. H., Armand, M., & Unberath, M. (2024) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2023.3299588 · Cited by 36 · see citing works
“Geodesic loss, L geo , which is the square of the geodesic distance in SE ( 3 ) , has been studied for registration problems due to its convexity with respect to pose transformations [32], [33].”
BackgroundAn enhanced marker pattern that achieves improved accuracy in surgical tool tracking
Cartucho, J., Wang, C., Huang, B., Elson, D. S., Darzi, A. W., & Giannarou, S. (2021) · Computer Methods in Biomechanics and Biomedical Engineering Imaging & Visualization · doi:10.1080/21681163.2021.1997647 · Cited by 36 · see citing works
Self-Supervised Viewpoint Learning From Image Collections
Mustikovela, S. K., Jampani, V., Mello, S. D., Liu, S., Iqbal, U., Rother, C., & Kautz, J. (2020) · 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/cvpr42600.2020.00403 · Cited by 35 · see citing works
Li, F., Wang, Y., Lü, H., Li, P., Li, Q., & Liu, F. (2020) · Journal of Physics G Nuclear and Particle Physics · doi:10.1088/1361-6471/abb1f9 · Cited by 35 · see citing works
Spin Detection in Robotic Table Tennis
Tebbe, J., Klamt, L., Gao, Y., & Zell, A. (2020) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra40945.2020.9196536 · Cited by 34 · see citing works
Cao, Z., Liu, D., Wang, Q., & Chen, Y. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-19775-8_43 · Cited by 34 · see citing works
Real-Time, Flight-Ready, Non-Cooperative Spacecraft Pose Estimation Using Monocular ImageryPreprint
Black, K., Shankar, S., Fonseka, D., Deutsch, J., Dhir, A., & Akella, M. R. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2101.09553 · Cited by 32 · see citing works
Deep neural networks for human pose estimation from a very low resolution depth image
Szczuko, P. (2019) · Multimedia Tools and Applications · doi:10.1007/s11042-019-7433-7 · Cited by 31 · see citing works
3D Pose Estimation for Fine-Grained Object Categories
Wang, Y., Tan, X., Yang, Y., Liu, X., Ding, E., Zhou, F., & Davis, L. S. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-11009-3_38 · Cited by 30 · see citing works
Vision-Based Sensing for Electrically-Driven Soft Actuators
Zhang, A., Truby, R. L., Chin, L., Li, S., & Rus, D. L. (2022) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2022.3201604 · Cited by 30 · see citing works
A Mixed Classification-Regression Framework for 3D Pose Estimation from 2D ImagesPreprint
Mahendran, S., Ali, H., & Vidal, R. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1805.03225 · Cited by 28 · see citing works
Self-citationProbabilistic orientation estimation with matrix Fisher distributionsPreprint
Mohlin, D., Bianchi, G., & Sullivan, J. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2006.09740 · Cited by 28 · see citing works
Object Pose Estimation from Monocular Image Using Multi-view Keypoint Correspondence
Kundu, J. N., Rahul, M., Ganeshan, A., & Babu, R. V. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-11015-4_23 · Cited by 27 · see citing works
Learning Local RGB-to-CAD Correspondences for Object Pose Estimation
Georgakis, G., Karanam, S., Wu, Z., & Košecká, J. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00906 · Cited by 27 · see citing works
Xiao, Y., Du, Y., & Marlet, R. (2021) · International Conference on 3D Vision (3DV) · doi:10.1109/3dv53792.2021.00018 · Cited by 26 · see citing works
Shi, W., Xu, H., Sun, C., Sun, J., Li, Y., Xu, X., Zheng, T., Zhang, Y., Wang, G., & Wu, D. (2022) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2022.3208277 · Cited by 26 · see citing works
Benchmarking Domain Randomisation for Visual Sim-to-Real Transfer
Alghonaim, R., & Johns, E. (2021) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra48506.2021.9561134 · Cited by 26 · see citing works
Regression-Based Three-Dimensional Pose Estimation for Texture-Less Objects
Liu, Y., Zhou, L., Zong, H., Gong, X., Wu, Q., Liang, Q., & Wang, J. (2019) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2019.2913321 · Cited by 25 · see citing works
3D Object Pose Estimation Using Multi-Objective Quaternion Learning
Papaioannidis, C., & Pitas, I. (2019) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2019.2929600 · Cited by 24 · see citing works
Domain-Translated 3D Object Pose Estimation
Papaioannidis, C., Mygdalis, V., & Pitas, I. (2020) · IEEE Transactions on Image Processing · doi:10.1109/tip.2020.3025447 · Cited by 24 · see citing works
SU-Net: pose estimation network for non-cooperative spacecraft on-orbit
Gao, H., Li, Z., Wang, N., Yang, J., & Dang, D. (2023) · Scientific Reports · doi:10.1038/s41598-023-38974-1 · Cited by 23 · see citing works
Contact2Grasp: 3D Grasp Synthesis via Hand-Object Contact Constraint
Li, H., Lin, X., Zhou, Y. M., Li, X., Huo, Y., Chen, J., & Ye, Q. (2023) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2023/117 · Cited by 23 · see citing works
Learning Deep Network for Detecting 3D Object Keypoints and 6D Poses
Zhao, W., Zhang, S., Guan, Z., Zhao, W., Peng, J., & Jianping Fan, B. (2020) · 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/cvpr42600.2020.01414 · Cited by 23 · see citing works
Jo, H., & Kim, E. (2020) · IEEE Access · doi:10.1109/access.2020.2988464 · Cited by 22 · see citing works
Visual Collaboration Leader-Follower UAV-Formation for Indoor Exploration
Evangeliou, N., Chaikalis, D., Τσουκαλάς, Α., & Tzes, A. P. (2022) · Frontiers in Robotics and AI · doi:10.3389/frobt.2021.777535 · Cited by 22 · see citing works
Enhancing object pose estimation for RGB images in cluttered scenes
Al-Selwi, M., Huang, N., Yin, G., Chao, Y., Li, Q., & Li, J. (2025) · Scientific Reports · doi:10.1038/s41598-025-90482-6 · Cited by 21 · see citing works
Object Pose Estimation using Mid-level Visual Representations
Nejatishahidin, N., Fayyazsanavi, P., & Košecká, J. (2022) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros47612.2022.9981452 · Cited by 20 · see citing works
Pérez-Villar, J. I. B., García‐Martín, Á., & Bescós, J. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-25056-9_3 · Cited by 20 · see citing works
SGRec3D: Self-Supervised 3D Scene Graph Learning via Object-Level Scene Reconstruction
Koch, S., Hermosilla, P., Vaškevičius, N., Colosi, M., & Ropinski, T. (2024) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv57701.2024.00337 · Cited by 20 · see citing works
Occlusion Resistant Object Rotation Regression from Point Cloud Segments
Gao, G., Lauri, M., Zhang, J., & Frintrop, S. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-11009-3_44 · Cited by 20 · see citing works
A Geometric Approach to Obtain a Bird's Eye View from an ImagePreprint
Abbas, A. N., & Zisserman, A. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1905.02231 · Cited by 20 · see citing works
Pose-guided Auto-Encoder and Feature-Based Refinement for 6-DoF Object Pose Regression
Li, Z., & Ji, X. (2020) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra40945.2020.9196953 · Cited by 19 · see citing works
Upper Body Pose Estimation Using Deep Learning for a Virtual Reality Avatar
Anvari, T., Park, K., & Kim, G. (2023) · Applied Sciences · doi:10.3390/app13042460 · Cited by 18 · see citing works
Deep-Learning-Based Direct Attitude Estimation for Uncooperative Known Space Objects
Qiao, S., Zhang, H., Xie, F., & Jiang, Z. (2023) · IEEE Transactions on Aerospace and Electronic Systems · doi:10.1109/taes.2023.3325801 · Cited by 19 · see citing works
Uncertainty-Aware Self-Supervised Learning of Spatial Perception Tasks
Nava, M., Paolillo, A., Guzzi, J., Gambardella, L. M., & Giusti, A. (2021) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2021.3095269 · Cited by 18 · see citing works
L6DNet: Light 6 DoF Network for Robust and Precise Object Pose Estimation With Small Datasets
Gonzalez, M., Kacete, A., Murienne, A., & Marchand, É. (2021) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2021.3062605 · Cited by 18 · see citing works
Deep learning-based plane pose regression in obstetric ultrasound
Di Vece, C., Dromey, B., Vasconcelos, F., David, A. L., Peebles, D., & Stoyanov, D. (2022) · International Journal of Computer Assisted Radiology and Surgery · doi:10.1007/s11548-022-02609-z · Cited by 17 · see citing works
Robust Six Degrees of Freedom Estimation for IIoT Based on Multibranch Network
Yang, J., Xi, M., Jiang, B., & Song, H. H. (2020) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2020.2982703 · Cited by 17 · see citing works
Ultra-low Power Deep Learning-based Monocular Relative Localization Onboard Nano-quadrotors
Bonato, S., Lambertenghi, S. C., Cereda, E., Giusti, A., & Palossi, D. (2023) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra48891.2023.10161127 · Cited by 16 · see citing works
KVNet: An iterative 3D keypoints voting network for real-time 6-DoF object pose estimation
Wang, F., Zhang, X., Chen, T., Shen, Z., Liu, S., & He, Z. (2023) · Neurocomputing · doi:10.1016/j.neucom.2023.01.036 · Cited by 15 · see citing works
Canonical Voting: Towards Robust Oriented Bounding Box Detection in 3D Scenes
You, Y., Ye, Z., Lou, Y., Li, C., Li, Y., Ma, L., Wang, W., & Lu, C. (2022) · 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/cvpr52688.2022.00126 · Cited by 14 · see citing works
Albanis, G., Zioulis, N., Dimou, A., Zarpalas, D., & Daras, P. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-66096-3_44 · Cited by 14 · see citing works
Vision-based attitude estimation for spacecraft docking operation through deep learning algorithm
Phisannupawong, T., Kamsing, P., Tortceka, P., & Yooyen, S. (2020) · International Conference on Advanced Communication Technology · doi:10.23919/icact48636.2020.9061445 · Cited by 14 · see citing works
Pose Estimation for Non-Cooperative Rendezvous Using Neural Networks
Sharma, S., & D'Amico, S. (2019) · arXiv · Cited by 40 · see citing works
HCR-Net: A Hybrid of Classification and Regression Network for Object Pose Estimation
Wang, Z., Li, W., Kao, Y., Zou, D., Wang, Q., Ahn, M., & Hong, S. (2018) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2018/141 · Cited by 13 · see citing works
Vision-state Fusion: Improving Deep Neural Networks for Autonomous Robotics
Cereda, E., Bonato, S., Nava, M., Giusti, A., & Palossi, D. (2024) · Journal of Intelligent & Robotic Systems · doi:10.1007/s10846-024-02091-6 · Cited by 12 · see citing works
Reconstructing the 3D digital core with a fully convolutional neural network
Li, Q., Chen, Z., He, J., Hao, S., Wang, R., Yang, H.-T., & Sun, H. (2020) · Applied Geophysics · doi:10.1007/s11770-020-0822-x · Cited by 12 · see citing works
Zhu, Y., Gao, R., Huang, S., Zhu, S. C., & Wu, Y. N. (2021) · 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/cvpr46437.2021.00983 · Cited by 12 · see citing works
Efficient 6-DoF Tracking of Handheld Objects from an Egocentric Viewpoint
Pandey, R. K., Pidlypenskyi, P., Yang, S., & Kaeser‐Chen, C. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01216-8_26 · Cited by 12 · see citing works
Novel Object Viewpoint Estimation Through Reconstruction Alignment
Banani, M. E., Corso, J. J., & Fouhey, D. F. (2020) · 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/cvpr42600.2020.00318 · Cited by 11 · see citing works
Matching RGB Images to CAD Models for Object Pose Estimation.Preprint
Georgakis, G., Karanam, S., Wu, Z., & Košecká, J. (2018) · arXiv (Cornell University) · Cited by 11 · see citing works
A Passenger Detection and Action Recognition System for Public Transport Vehicles
Lin, H., Kao, S.-F., & Wang, C. (2024) · Journal of Intelligent & Robotic Systems · doi:10.1007/s10846-024-02194-0 · Cited by 10 · see citing works
Arza-García, M., Gonçalves, J. A., Pinto, V. F., & Bastos, G. (2024) · Remote Sensing · doi:10.3390/rs16020331 · Cited by 10 · see citing works
Optimal STEM Convergence Angle Selection Using a Convolutional Neural Network and the Strehl Ratio
Schnitzer, N., Sung, S. H., & Hovden, R. (2020) · Microscopy and Microanalysis · doi:10.1017/s1431927620001841 · Cited by 10 · see citing works
Μαρούγκας, Ι., Koutras, P., Kardaris, N., Retsinas, G., Chalvatzaki, G., & Maragos, P. A. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-66096-3_45 · Cited by 10 · see citing works
Kubeyev, A. (2023) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2023.106562 · Cited by 9 · see citing works
mmBaT: A Multi-Task Framework for Mmwave-Based Human Body Reconstruction and Translation Prediction
Yang, J., Xia, S., Song, Y., Wu, Q., & Pei, L. (2024) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp48485.2024.10448164 · Cited by 9 · see citing works
Learning Cross-View Consistent 3D Keypoints for Object 6D Pose Estimation
Zhang, S., Zhao, W., Guan, Z., Zhao, W. Z., Peng, J., & Jianping Fan, B. (2025) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2025.3538018 · Cited by 8 · see citing works
Chen, M., Zhang, Z., Gu, S., & Kong, Y. (2024) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp48485.2024.10446096 · Cited by 8 · see citing works
Monocular Viewpoints Estimation for Generic Objects in the Wild
Li, Z., Wang, Y., & Ji, X. (2019) · IEEE Access · doi:10.1109/access.2019.2923436 · Cited by 8 · see citing works
Cutting Pose Prediction from Point Clouds
Philipsen, M. P., & Moeslund, T. B. (2020) · Sensors · doi:10.3390/s20061563 · Cited by 8 · see citing works
Giefer, L. A., Castellanos, J. D. A., Faghihabdolahi, M., & Freitag, M. (2020) · Procedia CIRP · doi:10.1016/j.procir.2020.05.092 · Cited by 8 · see citing works
- Authors
- Mahendran, S., Ali, H., & Vidal, R.
- Affiliations
- Johns Hopkins University (US)
- ORCID
- Ali, Haider
- Venue
- 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
- Publisher
- IEEE
- Published
- 2017-07
- Pages
- 494-495
- OpenAlex
- W2737704667
- Cited by
- 210
- References
- 24
- Citations by year
- 2026: 6 · 2025: 9 · 2024: 20 · 2023: 24 · 2022: 23 · 2021: 33 · 2020: 45 · 2019: 32 · 2018: 17 · 2017: 1
- FWCI
- 7.60
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) (publishedVersion)
- IEEE International Conference on Computer Vision Workshops (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- National Science Foundation (1527340, 1053575, 1445606)
- Maryland Advanced Research Computing Center
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Engineering › Control and Systems Engineering › Robot Manipulation and Learning
- Physical Sciences › Engineering › Computational Mechanics › 3D Shape Modeling and Analysis
- Keywords
- 3D pose estimation, object pose estimation, data augmentation, loss function design
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
