Fetching the paper…
Reading the bibliography…
We introduce SE3-Nets, which are deep neural networks designed to model and learn rigid body motion from raw point cloud data.
R. Baillargeon, “Infants’ physical world,”
2004
Earlier work this paper cites.
M. Toussaint, “Robot trajectory optimization using approximate inference,” in
2009
Earlier work this paper cites.
M. Deisenroth and C. E. Rasmussen, “Pilco: A model-based and data-efficient approach to policy search,” in
2011
Earlier work this paper cites.
R. Collobert, K. Kavukcuoglu, and C. Farabet, “Torch7: A matlab-like environment for machine learning,” in
2011
Earlier work this paper cites.
E. Todorov, T. Erez, and Y. Tassa, “Mujoco: A physics engine for model-based control,” in
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
2012
Earlier work this paper cites.
P. W. Battaglia, J. B. Hamrick, and J. B. Tenenbaum, “Simulation as an engine of physical scene understanding,”
2013
Earlier work this paper cites.
A. N. Sanborn, V. K. Mansinghka, and T. L. Griffiths, “Reconciling intuitive physics and newtonian mechanics for colliding objects.”
2013
Earlier work this paper cites.
P. Geoffroy, N. Mansard, M. Raison, S. Achiche, and E. Todorov, “From inverse kinematics to optimal control,” in
2014
Earlier work this paper cites.
B. Boots, A. Byravan, and D. Fox, “Learning predictive models of a depth camera & manipulator from raw execution traces,” in
2014
Earlier work this paper cites.
J. Ba, V. Mnih, and K. Kavukcuoglu, “Multiple object recognition with visual attention,”
2014
Earlier work this paper cites.
D. Kingma and J. Ba, “Adam: A method for stochastic optimization,”
2014
Cited alongside, same era.
T. Schmidt, R. A. Newcombe, and D. Fox, “Dart: Dense articulated real-time tracking.” in
2014
Cited alongside, same era.
M. Watter, J. Springenberg, J. Boedecker, and M. Riedmiller, “Embed to control: A locally linear latent dynamics model for control from raw images,” in
2015
Cited alongside, same era.
2015
Cited alongside, same era.
2015
2015
Later among the works it cites.
J. Zhou, R. Paolini, J. A. Bagnell, and M. T. Mason, “A convex polynomial force-motion model for planar sliding: Identification and application.” IEEE, 2016
2016
Closest in time.
W. Whitney, “Disentangled representations in neural models,”
2016
Closest in time.
M. Kopicki, S. Zurek, R. Stolkin, T. Moerwald, and J. L. Wyatt, “Learning modular and transferable forward models of the motions of push manipulated objects,”
2016
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
P. Agrawal, J. Carreira, and J. Malik, “Learning to see by moving,” in
2015
Cited alongside, same era.
T. D. Kulkarni, W. F. Whitney, P. Kohli, and J. Tenenbaum, “Deep convolutional inverse graphics network,” in
2015
Cited alongside, same era.
J. Yang, S. E. Reed, M.-H. Yang, and H. Lee, “Weakly-supervised disentangling with recurrent transformations for 3d view synthesis,” in
2015
Cited alongside, same era.
2015
Cited alongside, same era.
J. Long, E. Shelhamer, and T. Darrell, “Fully convolutional networks for semantic segmentation,” in
2015
Cited alongside, same era.
2015
Cited alongside, same era.
2016
Closest in time.
A. Lerer, S. Gross, and R. Fergus, “Learning physical intuition of block towers by example,”
2016
Closest in time.
R. Mottaghi, H. Bagherinezhad, M. Rastegari, and A. Farhadi, “Newtonian scene understanding: Unfolding the dynamics of objects in static images,” in
2016
Closest in time.
C. Finn, I. Goodfellow, and S. Levine, “Unsupervised learning for physical interaction through video prediction,” in
2016
Closest in time.
A. Handa, M. Bloesch, V. Pătrăucean, S. Stent, J. McCormac, and A. Davison, “gvnn: Neural network library for geometric computer vision,” in
2016
Closest in time.
M. Jaderberg, K. Simonyan, A. Zisserman,
2016
Closest in time.