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Humans have a strong intuitive understanding of the 3D environment around us.
Feedback control of dynamic systems , volume 4
G. F. Franklin, J. D. Powell, A. Emami-Naeini, and J. D. Powell · 2002
Earlier work this paper cites.
Apriltag: A robust and flexible visual fiducial system
E. Olson · 2011
Earlier work this paper cites.
Unified particle physics for real-time applications
M. Macklin, M. Müller, N. Chentanez, and T.-Y. Kim · 2014
Earlier work this paper cites.
Embed to control: A locally linear latent dynamics model for control from raw images
M. Watter, J. T. Springenberg, J. Boedecker, and M. Riedmiller · 2015
Earlier work this paper cites.
Single-view to multi-view: Reconstructing unseen views with a convolutional network
M. Tatarchenko, A. Dosovitskiy, and T. Brox · 2015
Earlier work this paper cites.
Model predictive path integral control using covariance variable importance sampling
G. Williams, A. Aldrich, and E. Theodorou · 2015
Earlier work this paper cites.
Unsupervised learning for physical interaction through video prediction
C. Finn, I. Goodfellow, and S. Levine · 2016
Earlier work this paper cites.
Learning to poke by poking: Experiential learning of intuitive physics
P. Agrawal, A. Nair, P. Abbeel, J. Malik, and S. Levine · 2016
Earlier work this paper cites.
Unsupervised learning of 3d structure from images
D. J. Rezende, S. Eslami, S. Mohamed, P. Battaglia, M. Jaderberg, and N. Heess · 2016
Earlier work this paper cites.
Feedback control of the pusher-slider system: A story of hybrid and underactuated contact dynamics
F. R. Hogan and A. Rodriguez · 2016
Earlier work this paper cites.
Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
Earlier work this paper cites.
Self-supervised visual planning with temporal skip connections
F. Ebert, C. Finn, A. X. Lee, and S. Levine · 2017
Earlier work this paper cites.
Interpretable transformations with encoder-decoder networks
D. E. Worrall, S. J. Garbin, D. Turmukhambetov, and G. J. Brostow · 2017
Earlier work this paper cites.
Reasoning about liquids via closed-loop simulation
C. Schenck and D. Fox · 2017
Earlier work this paper cites.
Visual foresight: Model-based deep reinforcement learning for vision-based robotic control
F. Ebert, C. Finn, S. Dasari, A. Xie, A. Lee, and S. Levine · 2018
Earlier work this paper cites.
Time-contrastive networks: Self-supervised learning from video
P. Sermanet, C. Lynch, Y. Chebotar, J. Hsu, E. Jang, S. Schaal, S. Levine, and G. Brain · 2018
Earlier work this paper cites.
Neural scene representation and rendering
S. A. Eslami, D. J. Rezende, F. Besse, F. Viola, A. S. Morcos, M. Garnelo, A. Ruderman, A. A. Rusu, I. Danihelka, K. Gregor, et al · 2018
Earlier work this paper cites.
Rendernet: A deep convolutional network for differentiable rendering from 3d shapes
T. Nguyen-Phuoc, C. Li, S. Balaban, and Y.-L. Yang · 2018
Earlier work this paper cites.
Visual object networks: image generation with disentangled 3d representations
J.-Y. Zhu, Z. Zhang, C. Zhang, J. Wu, A. Torralba, J. Tenenbaum, and B. Freeman · 2018
Earlier work this paper cites.
A data-efficient approach to precise and controlled pushing
M. Bauza, F. R. Hogan, and A. Rodriguez · 2018
Earlier work this paper cites.
Perceiving and reasoning about liquids using fully convolutional networks
C. Schenck and D. Fox · 2018
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Dream to control: Learning behaviors by latent imagination
D. Hafner, T. Lillicrap, J. Ba, and M. Norouzi · 2019
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kpam: Keypoint affordances for category-level robotic manipulation
L. Manuelli, W. Gao, P. Florence, and R. Tedrake · 2019
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Learning latent dynamics for planning from pixels
D. Hafner, T. Lillicrap, I. Fischer, R. Villegas, D. Ha, H. Lee, and J. Davidson · 2019
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Deepvoxels: Learning persistent 3d feature embeddings
V. Sitzmann, J. Thies, F. Heide, M. Nießner, G. Wetzstein, and M. Zollhöfer · 2019
Cited alongside, same era.
6-pack: Category-level 6d pose tracker with anchor-based keypoints
C. Wang, R. Martín-Martín, D. Xu, J. Lv, C. Lu, L. Fei-Fei, S. Savarese, and Y. Zhu · 2020
Later among the works it cites.
Causal discovery in physical systems from videos
Y. Li, A. Torralba, A. Anandkumar, D. Fox, and A. Garg · 2020
Later among the works it cites.
Nerf: Representing scenes as neural radiance fields for view synthesis
B. Mildenhall, P. P. Srinivasan, M. Tancik, J. T. Barron, R. Ramamoorthi, and R. Ng · 2020
Later among the works it cites.
3d-oes: Viewpoint-invariant object-factorized environment simulators
H.-Y. F. Tung, Z. Xian, M. Prabhudesai, S. Lal, and K. Fragkiadaki · 2020
Later among the works it cites.
Visual grounding of learned physical models
Y. Li, T. Lin, K. Yi, D. Bear, D. Yamins, J. Wu, J. Tenenbaum, and A. Torralba · 2020
Later among the works it cites.
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S. Lombardi, T. Simon, J. Saragih, G. Schwartz, A. Lehrmann, and Y. Sheikh · 2019
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Scene representation networks: Continuous 3d-structure-aware neural scene representations
V. Sitzmann, M. Zollhöfer, and G. Wetzstein · 2019
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Learning spatial common sense with geometry-aware recurrent networks
H.-Y. F. Tung, R. Cheng, and K. Fragkiadaki · 2019
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Deepsdf: Learning continuous signed distance functions for shape representation
J. J. Park, P. Florence, J. Straub, R. Newcombe, and S. Lovegrove · 2019
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Learning spatial common sense with geometry-aware recurrent networks
H.-Y. F. Tung, R. Cheng, and K. Fragkiadaki · 2019
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Beyond the self: Using grounded affordances to interpret and describe others’ actions
G. Saponaro, L. Jamone, A. Bernardino, and G. Salvi · 2019
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Pifu: Pixel-aligned implicit function for high-resolution clothed human digitization
S. Saito, Z. Huang, R. Natsume, S. Morishima, A. Kanazawa, and H. Li · 2019
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A. Trevithick and B. Yang · 2020
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pixelnerf: Neural radiance fields from one or few images
A. Yu, V. Ye, M. Tancik, and A. Kanazawa · 2020
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Metasdf: Meta-learning signed distance functions
V. Sitzmann, E. R. Chan, R. Tucker, N. Snavely, and G. Wetzstein · 2020
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Deformable neural radiance fields
K. Park, U. Sinha, J. T. Barron, S. Bouaziz, D. B. Goldman, S. M. Seitz, and R.-M. Brualla · 2020
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D-nerf: Neural radiance fields for dynamic scenes
A. Pumarola, E. Corona, G. Pons-Moll, and F. Moreno-Noguer · 2020
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E. Tretschk, A. Tewari, V. Golyanik, M. Zollhöfer, C. Lassner, and C. Theobalt · 2020
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Neural scene flow fields for space-time view synthesis of dynamic scenes
Z. Li, S. Niklaus, N. Snavely, and O. Wang · 2020
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Neural radiance flow for 4d view synthesis and video processing
Y. Du, Y. Zhang, H.-X. Yu, J. B. Tenenbaum, and J. Wu · 2020
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Deep dynamics models for learning dexterous manipulation
A. Nagabandi, K. Konolige, S. Levine, and V. Kumar · 2020
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Differentiable volumetric rendering: Learning implicit 3d representations without 3d supervision
M. Niemeyer, L. Mescheder, M. Oechsle, and A. Geiger · 2020
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kpam 2.0: Feedback control for category-level robotic manipulation
W. Gao and R. Tedrake · 2021
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Space-time neural irradiance fields for free-viewpoint video
W. Xian, J.-B. Huang, J. Kopf, and C. Kim · 2021
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Neural scene graphs for dynamic scenes
J. Ost, F. Mannan, N. Thuerey, J. Knodt, and F. Heide · 2021
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T. Li, M. Slavcheva, M. Zollhoefer, S. Green, C. Lassner, C. Kim, T. Schmidt, S. Lovegrove, M. Goesele, and Z. Lv · 2021
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