Fetching the paper…
Reading the bibliography…
This work provides an architecture to enable robotic grasp planning via shape completion.
Z. Wu, S. Song, A. Khosla, F. Yu, L. Zhang, X. Tang, and J. Xiao, “3d shapenets: A deep representation for volumetric shapes,” in CVPR , 2015, pp. 1912–1920
1920
Earlier work this paper cites.
J. Mahler, F. T. Pokorny, B. Hou, M. Roderick, M. Laskey, M. Aubry, K. Kohlhoff, T. Kröger, J. Kuffner, and K. Goldberg, “Dex-net 1.0: A cloud-based network of 3d objects for robust grasp planning using a multi-armed bandit model with correlated rewards,” in ICRA, 2016 . IEEE, 2016, pp. 1957–1964
1964
Earlier work this paper cites.
W. E. Lorensen and H. E. Cline, “Marching cubes: A high resolution 3d surface construction algorithm,” in ACM siggraph computer graphics , vol. 21, no. 4. ACM, 1987, pp. 163–169
1987
Earlier work this paper cites.
Y. LeCun and Y. Bengio, “Convolutional networks for images, speech, and time series,” in Handbk. of brain theory & neural networks , 1995
1995
Earlier work this paper cites.
A. B. Hamza and H. Krim, “Geodesic object representation and recognition,” in International conference on discrete geometry for computer imagery . Springer, 2003, pp. 378–387
2003
Earlier work this paper cites.
P. Min, “Binvox, a 3d mesh voxelizer,” 2004
2004
Earlier work this paper cites.
N. Koenig and A. Howard, “Design and use paradigms for Gazebo, an open-source multi-robot simulator,” in IROS , vol. 3. IEEE, 2004, pp. 2149–2154
2004
Earlier work this paper cites.
A. T. Miller and P. K. Allen, “Graspit! a versatile simulator for robotic grasping,” IEEE R&A Magazine , vol. 11, no. 4, pp. 110–122, 2004
2004
Earlier work this paper cites.
P. Cignoni, M. Corsini, and G. Ranzuglia, “Meshlab: an open-source 3d mesh processing system,” Ercim news , vol. 73, pp. 45–46, 2008
2008
Earlier work this paper cites.
C. Goldfeder, M. Ciocarlie, H. Dang, and P. K. Allen, “The columbia grasp database,” in ICRA . IEEE, 2009, pp. 1710–1716
2009
Earlier work this paper cites.
C. Papazov and D. Burschka, “An efficient ransac for 3d object recognition in noisy and occluded scenes,” in Asian Conference on Computer Vision . Springer, 2010, pp. 135–148
2010
Earlier work this paper cites.
J. Bergstra, O. Breuleux, F. Bastien, P. Lamblin, R. Pascanu, G. Desjardins, J. Turian, D. Warde-Farley, and Y. Bengio, “Theano: a cpu and gpu math expression compiler,” in Proceedings of the Python for scientific computing conference (SciPy) , vol. 4. Austin, TX, 2010
2010
Earlier work this paper cites.
X. Glorot and Y. Bengio, “Understanding the difficulty of training deep feedforward neural networks,” in Proceedings of the 13th AISTATS , 2010, pp. 249–256
2010
Earlier work this paper cites.
V. Lempitsky, “Surface extraction from binary volumes with higher-order smoothness,” in Computer Vision and Pattern Recognition (CVPR), 2010 IEEE Conference on . IEEE, 2010, pp. 1197–1204
2010
Cited alongside, same era.
J. Bohg, M. Johnson-Roberson, B. León, J. Felip, X. Gratal, N. Bergström, D. Kragic, and A. Morales, “Mind the gap-robotic grasping under incomplete observation,” in ICRA . IEEE, 2011, pp. 686–693
2011
Cited alongside, same era.
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 ICCV), 2011 . IEEE, 2011, pp. 858–865
2011
Cited alongside, same era.
R. B. Rusu and S. Cousins, “3D is here: Point Cloud Library (PCL),” in ICRA , Shanghai, China, May 9-13 2011
2011
Cited alongside, same era.
J. Rock, T. Gupta, J. Thorsen, J. Gwak, D. Shin, and D. Hoiem, “Completing 3d object shape from one depth image,” in CVPR , 2015, pp. 2484–2493
2015
Later among the works it cites.
Y. Li, A. Dai, L. Guibas, and M. Nießner, “Database-assisted object retrieval for real-time 3d reconstruction,” in Computer Graphics Forum , vol. 34, no. 2. Wiley Online Library, 2015, pp. 435–446
2015
Later among the works it cites.
J. Varley, J. Weisz, J. Weiss, and P. Allen, “Generating multi-fingered robotic grasps via deep learning,” in IROS , 2015, pp. 4415–4420
2015
Later among the works it cites.
B. Calli, A. Singh, A. Walsman, S. Srinivasa, P. Abbeel, and A. M. Dollar, “The ycb object and model set: Towards common benchmarks for manipulation research,” in Advanced Robotics (ICAR), 2015 International Conference on . IEEE, 2015, pp. 510–517
2015
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2012
Cited alongside, same era.
I. A. Sucan and S. Chitta, “Moveit!” http://moveit.ros.org , 2013
2013
Cited alongside, same era.
2014
Cited alongside, same era.
Y. Xiang, R. Mottaghi, and S. Savarese, “Beyond pascal: A benchmark for 3d object detection in the wild,” in WACV , 2014
2014
Cited alongside, same era.
2014
Cited alongside, same era.
2014
Cited alongside, same era.
2015
Cited alongside, same era.
A. H. Quispe, B. Milville, M. A. Gutiérrez, C. Erdogan, M. Stilman, H. Christensen, and H. B. Amor, “Exploiting symmetries and extrusions for grasping household objects,” in ICRA , 2015, pp. 3702–3708
2015
Cited alongside, same era.
D. Kappler, J. Bohg, and S. Schaal, “Leveraging big data for grasp planning,” in ICRA . IEEE, 2015, pp. 4304–4311
2015
Later among the works it cites.
F. Chollet, “Keras,” https://github.com/fchollet/keras, 2015
2015
Later among the works it cites.
2015
Later among the works it cites.
D. Schiebener, A. Schmidt, N. Vahrenkamp, and T. Asfour, “Heuristic 3d object shape completion based on symmetry and scene context,” in IROS . IEEE, 2016, pp. 74–81
2016
Closest in time.
C. Rennie, R. Shome, K. E. Bekris, and A. F. De Souza, “A dataset for improved rgbd-based object detection and pose estimation for warehouse pick-and-place,” IEEE Robotics and Automation Letters , vol. 1, no. 2, pp. 1179–1185, 2016
2016
Closest in time.
M. Firman, O. Mac Aodha, S. Julier, and G. J. Brostow, “Structured prediction of unobserved voxels from a single depth image,” in CVPR , 2016, pp. 5431–5440
2016
Closest in time.
S. Tulsiani, A. Kar, J. Carreira, and J. Malik, “Learning category-specific deformable 3d models for object reconstruction,” IEEE transactions on pattern analysis and machine intelligence , 2016
2016
Closest in time.
C. B. Choy, D. Xu, J. Gwak, K. Chen, and S. Savarese, “3d-r2n2: A unified approach for single and multi-view 3d object reconstruction,” in ECCV . Springer, 2016, pp. 628–644
2016
Closest in time.