publication . Preprint . 2018

Deep Object Pose Estimation for Semantic Robotic Grasping of Household Objects

Tremblay, Jonathan; To, Thang; Sundaralingam, Balakumar; Xiang, Yu; Fox, Dieter; Birchfield, Stan;
Open Access English
  • Published: 27 Sep 2018
Abstract
Comment: Conference on Robot Learning (CoRL) 2018
Subjects
free text keywords: Computer Science - Robotics
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48 references, page 1 of 4

[1] J. Dai, Y. Li, K. He, and J. Sun. R-FCN: Object detection via region-based fully convolutional networks. In NIPS, 2016.

[2] J. Redmon and A. Farhadi. YOLO9000: Better, faster, stronger. In CVPR, 2017.

[3] W. Liu, D. Anguelov, D. Erhan, C. Szegedy, S. Reed, C.-Y. Fu, and A. C. Berg. SSD: Single shot multibox detector. In ECCV, 2016.

[4] M. Rad and V. Lepetit. BB8: A scalable, accurate, robust to partial occlusion method for predicting the 3D poses of challenging objects without using depth. In ICCV, 2017.

[5] Y. Xiang, T. Schmidt, V. Narayanan, and D. Fox. PoseCNN: A convolutional neural network for 6D object pose estimation in cluttered scenes. In RSS, 2018.

[6] B. Tekin, S. N. Sinha, and P. Fua. Real-time seamless single shot 6D object pose prediction. In CVPR, 2018.

[7] J. Tobin, R. Fong, A. Ray, J. Schneider, W. Zaremba, and P. Abbeel. Domain randomization for transferring deep neural networks from simulation to the real world. In IROS, 2017.

[8] F. Sadeghi and S. Levine. CAD2RL: Real single-image flight without a single real image. In Robotics: Science and Systems (RSS), 2017.

[9] F. Zhang, J. Leitner, M. Milford, and P. Corke. Sim-to-real transfer of visuo-motor policies for reaching in clutter: Domain randomization and adaptation with modular networks. In arXiv 1709.05746, 2017. [OpenAIRE]

[10] S. James, A. J. Davison, and E. Johns. Transferring end-to-end visuomotor control from simulation to real world for a multi-stage task. In CoRL, 2017.

[11] J. Tremblay, T. To, and S. Birchfield. Falling things: A synthetic dataset for 3D object detection and pose estimation. In CVPR Workshop on Real World Challenges and New Benchmarks for Deep Learning in Robotic Vision, June 2018.

[12] V. Lepetit, F. Moreno-Noguer, and P. Fua. EPnP: An accurate O(n) solution to the PnP problem. International Journal Computer Vision, 81(2), 2009.

[13] S.-E. Wei, V. Ramakrishna, T. Kanade, and Y. Sheikh. Convolutional pose machines. In CVPR, 2016.

[14] Z. Cao, T. Simon, S.-E. Wei, and Y. Sheikh. Realtime multi-person 2D pose estimation using part affinity fields. In CVPR, 2017.

[15] K. Simonyan and A. Zisserman. Very deep convolutional networks for large-scale image recognition. In ICLR, 2015.

48 references, page 1 of 4
Abstract
Comment: Conference on Robot Learning (CoRL) 2018
Subjects
free text keywords: Computer Science - Robotics
Download from
48 references, page 1 of 4

[1] J. Dai, Y. Li, K. He, and J. Sun. R-FCN: Object detection via region-based fully convolutional networks. In NIPS, 2016.

[2] J. Redmon and A. Farhadi. YOLO9000: Better, faster, stronger. In CVPR, 2017.

[3] W. Liu, D. Anguelov, D. Erhan, C. Szegedy, S. Reed, C.-Y. Fu, and A. C. Berg. SSD: Single shot multibox detector. In ECCV, 2016.

[4] M. Rad and V. Lepetit. BB8: A scalable, accurate, robust to partial occlusion method for predicting the 3D poses of challenging objects without using depth. In ICCV, 2017.

[5] Y. Xiang, T. Schmidt, V. Narayanan, and D. Fox. PoseCNN: A convolutional neural network for 6D object pose estimation in cluttered scenes. In RSS, 2018.

[6] B. Tekin, S. N. Sinha, and P. Fua. Real-time seamless single shot 6D object pose prediction. In CVPR, 2018.

[7] J. Tobin, R. Fong, A. Ray, J. Schneider, W. Zaremba, and P. Abbeel. Domain randomization for transferring deep neural networks from simulation to the real world. In IROS, 2017.

[8] F. Sadeghi and S. Levine. CAD2RL: Real single-image flight without a single real image. In Robotics: Science and Systems (RSS), 2017.

[9] F. Zhang, J. Leitner, M. Milford, and P. Corke. Sim-to-real transfer of visuo-motor policies for reaching in clutter: Domain randomization and adaptation with modular networks. In arXiv 1709.05746, 2017. [OpenAIRE]

[10] S. James, A. J. Davison, and E. Johns. Transferring end-to-end visuomotor control from simulation to real world for a multi-stage task. In CoRL, 2017.

[11] J. Tremblay, T. To, and S. Birchfield. Falling things: A synthetic dataset for 3D object detection and pose estimation. In CVPR Workshop on Real World Challenges and New Benchmarks for Deep Learning in Robotic Vision, June 2018.

[12] V. Lepetit, F. Moreno-Noguer, and P. Fua. EPnP: An accurate O(n) solution to the PnP problem. International Journal Computer Vision, 81(2), 2009.

[13] S.-E. Wei, V. Ramakrishna, T. Kanade, and Y. Sheikh. Convolutional pose machines. In CVPR, 2016.

[14] Z. Cao, T. Simon, S.-E. Wei, and Y. Sheikh. Realtime multi-person 2D pose estimation using part affinity fields. In CVPR, 2017.

[15] K. Simonyan and A. Zisserman. Very deep convolutional networks for large-scale image recognition. In ICLR, 2015.

48 references, page 1 of 4
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