Fetching the paper…
Reading the bibliography…
Despite the recent progress on 6D object pose estimation methods for robotic grasping, a substantial performance gap persists between the capabilities of these methods on existing datasets and their efficacy in real-world grasping and mobile manipulation tasks, particularly when robots rely solely on their monocular egocentric field of view (FOV).
F. Jurie and M. Dhome, “A simple and efficient template matching algorithm,” in Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001 , vol. 2. IEEE, 2001, pp. 544–549
2001
Earlier work this paper cites.
S. Hinterstoisser, S. Holzer, C. Cagniart, S. Ilic, K. Konolige, N. Navab, and V. Lepetit, “Multimodal templates for real-time detection of texture-less objects in heavily cluttered scenes,” in 2011 international conference on computer vision . IEEE, 2011, pp. 858–865
2011
Earlier work this paper cites.
A. Collet, M. Martinez, and S. S. Srinivasa, “The moped framework: Object recognition and pose estimation for manipulation,” The international journal of robotics research , vol. 30, no. 10, pp. 1284–1306, 2011
2011
Earlier work this paper cites.
A. Kasper, Z. Xue, and R. Dillmann, “The kit object models database: An object model database for object recognition, localization and manipulation in service robotics,” The International Journal of Robotics Research , vol. 31, no. 8, pp. 927–934, 2012
2012
Earlier work this paper cites.
S. Hinterstoisser, V. Lepetit, S. Ilic, S. Holzer, G. Bradski, K. Konolige, and N. Navab, “Model based training, detection and pose estimation of texture-less 3d objects in heavily cluttered scenes,” in Computer Vision–ACCV 2012: 11th Asian Conference on Computer Vision, Daejeon, Korea, November 5-9, 2012, Revised Selected Papers, Part I 11 . Springer, 2013, pp. 548–562
2013
Earlier work this paper cites.
E. Brachmann, A. Krull, F. Michel, S. Gumhold, J. Shotton, and C. Rother, “Learning 6d object pose estimation using 3d object coordinates,” in Computer Vision–ECCV 2014: 13th European Conference, Zurich, Switzerland, September 6-12, 2014, Proceedings, Part II 13 . Springer, 2014, pp. 536–551
2014
Earlier work this paper cites.
A. Tejani, D. Tang, R. Kouskouridas, and T.-K. Kim, “Latent-class hough forests for 3d object detection and pose estimation,” in Computer Vision–ECCV 2014: 13th European Conference, Zurich, Switzerland, September 6-12, 2014, Proceedings, Part VI 13 . Springer, 2014, pp. 462–477
2014
Earlier work this paper cites.
B. Calli, A. Walsman, A. Singh, S. Srinivasa, P. Abbeel, and A. M. Dollar, “Benchmarking in manipulation research: Using the yale-cmu-berkeley object and model set,” IEEE Robotics & Automation Magazine , vol. 22, no. 3, pp. 36–52, 2015
2015
Earlier work this paper cites.
K. Pauwels and D. Kragic, “Simtrack: A simulation-based framework for scalable real-time object pose detection and tracking,” in 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2015, pp. 1300–1307
2015
Earlier work this paper cites.
A. Doumanoglou, R. Kouskouridas, S. Malassiotis, and T.-K. Kim, “Recovering 6d object pose and predicting next-best-view in the crowd,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2016, pp. 3583–3592
2016
Earlier work this paper cites.
C. Mandery, O. Terlemez, M. Do, N. Vahrenkamp, and T. Asfour, “Unifying representations and large-scale whole-body motion databases for studying human motion,” IEEE Transactions on Robotics , vol. 32, no. 4, pp. 796–809, 2016
2016
Earlier work this paper cites.
T. Hodan, P. Haluza, Š. Obdržálek, J. Matas, M. Lourakis, and X. Zabulis, “T-less: An rgb-d dataset for 6d pose estimation of texture-less objects,” in 2017 IEEE Winter Conference on Applications of Computer Vision (WACV) . IEEE, 2017, pp. 880–888
2017
Earlier work this paper cites.
B. Drost, M. Ulrich, P. Bergmann, P. Hartinger, and C. Steger, “Introducing mvtec itodd-a dataset for 3d object recognition in industry,” in Proceedings of the IEEE international conference on computer vision workshops , 2017, pp. 2200–2208
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
P. Anderson, Q. Wu, D. Teney, J. Bruce, M. Johnson, N. Sünderhauf, I. Reid, S. Gould, and A. Van Den Hengel, “Vision-and-language navigation: Interpreting visually-grounded navigation instructions in real environments,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2018, pp. 3674–3683
2018
Earlier work this paper cites.
T. Hodan, F. Michel, E. Brachmann, W. Kehl, A. GlentBuch, D. Kraft, B. Drost, J. Vidal, S. Ihrke, X. Zabulis et al. , “Bop: Benchmark for 6d object pose estimation,” in Proceedings of the European conference on computer vision (ECCV) , 2018, pp. 19–34
2018
Earlier work this paper cites.
R. Kaskman, S. Zakharov, I. Shugurov, and S. Ilic, “Homebreweddb: Rgb-d dataset for 6d pose estimation of 3d objects,” in Proceedings of the IEEE/CVF International Conference on Computer Vision Workshops , 2019, pp. 0–0
2019
Earlier work this paper cites.
T. Asfour, M. Wächter, L. Kaul, S. Rader, P. Weiner, S. Ottenhaus, R. Grimm, Y. Zhou, M. Grotz, and F. Paus, “Armar-6: A high-performance humanoid for human-robot collaboration in real world scenarios,” IEEE Robotics & Automation Magazine , vol. 26, no. 4, pp. 108–121, 2019
2019
Earlier work this paper cites.
T. Asfour, M. Waechter, L. Kaul, S. Rader, P. Weiner, S. Ottenhaus, R. Grimm, Y. Zhou, M. Grotz, and F. Paus, “Armar-6: A high-performance humanoid for human-robot collaboration in real-world scenarios,” IEEE Robotics & Automation Magazine , vol. 26, no. 4, pp. 108–121, 2019
2019
Cited alongside, same era.
D. S. Chaplot, D. P. Gandhi, A. Gupta, and R. R. Salakhutdinov, “Object goal navigation using goal-oriented semantic exploration,” Advances in Neural Information Processing Systems , vol. 33, pp. 4247–4258, 2020
2020
Cited alongside, same era.
H.-S. Fang, C. Wang, M. Gou, and C. Lu, “Graspnet-1billion: A large-scale benchmark for general object grasping,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2020, pp. 11 444–11 453
2020
Cited alongside, same era.
M. Sundermeyer, M. Durner, E. Y. Puang, Z.-C. Marton, N. Vaskevicius, K. O. Arras, and R. Triebel, “Multi-path learning for object pose estimation across domains,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2020, pp. 13 916–13 925
Y. Hu, P. Fua, and M. Salzmann, “Perspective flow aggregation for data-limited 6d object pose estimation,” in European Conference on Computer Vision . Springer, 2022, pp. 89–106
2022
Later among the works it cites.
L. Chen, H. Yang, C. Wu, and S. Wu, “Mp6d: An rgb-d dataset for metal parts’ 6d pose estimation,” IEEE Robotics and Automation Letters , vol. 7, no. 3, pp. 5912–5919, 2022
2022
Later among the works it cites.
X. Chen, H. Zhang, Z. Yu, A. Opipari, and O. Chadwicke Jenkins, “Clearpose: Large-scale transparent object dataset and benchmark,” in European Conference on Computer Vision . Springer, 2022, pp. 381–396
2022
Later among the works it cites.
V. N. Nguyen, Y. Hu, Y. Xiao, M. Salzmann, and V. Lepetit, “Templates for 3d object pose estimation revisited: Generalization to new objects and robustness to occlusions,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2022, pp. 6771–6780
2022
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2020
Cited alongside, same era.
M. Tan, R. Pang, and Q. V. Le, “Efficientdet: Scalable and efficient object detection,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2020, pp. 10 781–10 790
2020
Cited alongside, same era.
2020
Cited alongside, same era.
T. Hodaň, M. Sundermeyer, B. Drost, Y. Labbé, E. Brachmann, F. Michel, C. Rother, and J. Matas, “Bop challenge 2020 on 6d object localization,” in Computer Vision–ECCV 2020 Workshops: Glasgow, UK, August 23–28, 2020, Proceedings, Part II 16 . Springer, 2020, pp. 577–594
2020
Cited alongside, same era.
J. Ye, D. Batra, E. Wijmans, and A. Das, “Auxiliary tasks speed up learning point goal navigation,” in Conference on Robot Learning . PMLR, 2021, pp. 498–516
2021
Cited alongside, same era.
S. Datta, O. Maksymets, J. Hoffman, S. Lee, D. Batra, and D. Parikh, “Integrating egocentric localization for more realistic point-goal navigation agents,” in Conference on Robot Learning . PMLR, 2021, pp. 313–328
2021
Cited alongside, same era.
A. Pal, Y. Qiu, and H. Christensen, “Learning hierarchical relationships for object-goal navigation,” in Conference on Robot Learning . PMLR, 2021, pp. 517–528
2021
Cited alongside, same era.
G. Wang, F. Manhardt, F. Tombari, and X. Ji, “Gdr-net: Geometry-guided direct regression network for monocular 6d object pose estimation,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2021, pp. 16 611–16 621
2021
Cited alongside, same era.
F. Krebs, A. Meixner, I. Patzer, and T. Asfour, “The kit bimanual manipulation dataset,” in IEEE/RAS International Conference on Humanoid Robots (Humanoids) , 2021, pp. 499–506
2021
Cited alongside, same era.
2022
Later among the works it cites.
2023
Later among the works it cites.
A. Younes, D. Honerkamp, T. Welschehold, and A. Valada, “Catch me if you hear me: Audio-visual navigation in complex unmapped environments with moving sounds,” IEEE Robotics and Automation Letters , vol. 8, no. 2, pp. 928–935, 2023
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
A. Guo, B. Wen, J. Yuan, J. Tremblay, S. Tyree, J. Smith, and S. Birchfield, “Handal: A dataset of real-world manipulable object categories with pose annotations, affordances, and reconstructions,” in 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2023, pp. 11 428–11 435
2023
Later among the works it cites.
M. Denninger, D. Winkelbauer, M. Sundermeyer, W. Boerdijk, M. Knauer, K. H. Strobl, M. Humt, and R. Triebel, “Blenderproc2: A procedural pipeline for photorealistic rendering,” Journal of Open Source Software , vol. 8, no. 82, p. 4901, 2023
2023
Later among the works it cites.
C. Pohl, F. Reister, F. Peller-Konrad, and T. Asfour, “MAkEable: Memory-centered and affordance-based task execution framework for transferable mobile manipulation skills,” in IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) , 2024
2024
Closest in time.
T. Birr, C. Pohl, A. Younes, and T. Asfour, “AutoGPT+P: Affordance-based task planning with large language models,” in Robotics Science and Systems (RSS) , 2024
2024
Closest in time.
2024
Closest in time.
R. Varghese and M. Sambath, “Yolov8: A novel object detection algorithm with enhanced performance and robustness,” in 2024 International Conference on Advances in Data Engineering and Intelligent Computing Systems (ADICS) . IEEE, 2024, pp. 1–6
2024
Closest in time.
A. Collet and S. S. Srinivasa, “Efficient multi-view object recognition and full pose estimation,” in 2010 IEEE International Conference on Robotics and Automation . IEEE, 2010, pp. 2050–2055
2055
Closest in time.