Fill and Transfer: A Simple Physics-Based Approach for Containability Reasoning
暂无分享,去创建一个
Sai Kit Yeung | Lap-Fai Yu | Noah Duncan | S. Yeung | N. Duncan | L. Yu | Sai-Kit Yeung
[1] Ashutosh Saxena,et al. Robotic Grasping of Novel Objects using Vision , 2008, Int. J. Robotics Res..
[2] Tsuhan Chen,et al. 3D-Based Reasoning with Blocks, Support, and Stability , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.
[3] Jessica B. Hamrick,et al. Simulation as an engine of physical scene understanding , 2013, Proceedings of the National Academy of Sciences.
[4] Katsushi Ikeuchi,et al. Beyond Point Clouds: Scene Understanding by Reasoning Geometry and Physics , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.
[5] Markus Vincze,et al. AfNet: The Affordance Network , 2012, Asian Conference on Computer Vision.
[6] Song-Chun Zhu,et al. Scene Parsing by Integrating Function, Geometry and Appearance Models , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.
[7] S. Waxman,et al. Object names and object functions serve as cues to categories for infants. , 2002, Developmental psychology.
[8] Michael Brünig,et al. Non-cubic occupied voxel lists for robot maps , 2009, 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems.
[9] Kenneth D. Forbus. Qualitative physics: past present and future , 1988 .
[10] Alexei A. Efros,et al. From 3D scene geometry to human workspace , 2011, CVPR 2011.
[11] Ehud Rivlin,et al. Functional 3D Object Classification Using Simulation of Embodied Agent , 2006, BMVC.
[12] Barbara Tversky,et al. Form and Function , 2005, Functional Features in Language and Space.
[13] Leonidas J. Guibas,et al. Shape2Pose , 2014, ACM Trans. Graph..
[14] Yun Jiang,et al. Hallucinated Humans as the Hidden Context for Labeling 3D Scenes , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.
[15] Yun Jiang,et al. Learning Object Arrangements in 3D Scenes using Human Context , 2012, ICML.
[16] Dieter Fox,et al. A large-scale hierarchical multi-view RGB-D object dataset , 2011, 2011 IEEE International Conference on Robotics and Automation.
[17] Martial Hebert,et al. Smoothing-based Optimization , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.
[18] G. Chirikjian. Stochastic models, information theory, and lie groups , 2012 .
[19] Chi-Keung Tang,et al. Make it home: automatic optimization of furniture arrangement , 2011, ACM Trans. Graph..
[20] Fei-Fei Li,et al. Discovering Object Functionality , 2013, 2013 IEEE International Conference on Computer Vision.
[21] Wei Liang,et al. Evaluating Human Cognition of Containing Relations with Physical Simulation , 2015, CogSci.
[22] Ashutosh Saxena,et al. Learning to open new doors , 2010, 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems.
[23] Michael Beetz,et al. Simulation-based temporal projection of everyday robot object manipulation , 2011, AAMAS.
[24] Li Fei-Fei,et al. Reasoning about Object Affordances in a Knowledge Base Representation , 2014, ECCV.
[25] Louis H. Sullivan,et al. The Tall Office Building Artistically Considered , 2012 .
[26] Lawson L. S. Wong,et al. Learning Grasp Strategies with Partial Shape Information , 2008, AAAI.
[27] Laura A. Carlson,et al. Functional Features in Language and Space - Insights from Perception, Categorization, and Development , 2005, Functional Features in Language and Space.
[28] Thomas A. Funkhouser,et al. The Princeton Shape Benchmark , 2004, Proceedings Shape Modeling Applications, 2004..
[29] J. Andrew Bagnell,et al. Perceiving, learning, and exploiting object affordances for autonomous pile manipulation , 2013, Auton. Robots.
[30] I. Holopainen. Riemannian Geometry , 1927, Nature.
[31] Michael Brünig,et al. Lattice occupied voxel lists for representation of spatial occupancy , 2010, 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems.
[32] G. Chirikjian. Stochastic Models, Information Theory, and Lie Groups, Volume 2 , 2012 .
[33] Lisa M Oakes,et al. Function revisited: how infants construe functional features in their representation of objects. , 2008, Advances in child development and behavior.
[34] Deva Ramanan,et al. Predicting Functional Regions on Objects , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition Workshops.
[35] William T. Freeman,et al. Estimating the Material Properties of Fabric from Video , 2013, 2013 IEEE International Conference on Computer Vision.
[36] Oliver Kroemer,et al. Generalizing pouring actions between objects using warped parameters , 2014, 2014 IEEE-RAS International Conference on Humanoid Robots.
[37] D. Cohen-Or,et al. Upright orientation of man-made objects , 2008, SIGGRAPH 2008.
[38] Hema Swetha Koppula,et al. Anticipating Human Activities Using Object Affordances for Reactive Robotic Response , 2013, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[39] Luc Van Gool,et al. What makes a chair a chair? , 2011, CVPR 2011.
[40] John J. Leonard,et al. A Mixture of Manhattan Frames: Beyond the Manhattan World , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.
[41] Dennis C Harrison. Form and function , 2012, Canadian Medical Association Journal.
[42] J. Greeno. Gibson's affordances. , 1994, Psychological review.
[43] Katsushi Ikeuchi,et al. Detecting potential falling objects by inferring human action and natural disturbance , 2014, 2014 IEEE International Conference on Robotics and Automation (ICRA).
[44] 三嶋 博之. The theory of affordances , 2008 .
[45] Hema Swetha Koppula,et al. Physically Grounded Spatio-temporal Object Affordances , 2014, ECCV.