Context-Based Vision: Recognizing Objects Using Information from Both 2D and 3D Imagery
IEEE Transactions on Pattern Analysis and Machine Intelligence - Special issue on interpretation of 3-D scenes—part I
ACM Transactions on Graphics (TOG)
Detection and Evaluation of Grasping Positions for Autonomous Agents
CW '05 Proceedings of the 2005 International Conference on Cyberworlds
Spherical Wavelet Descriptors for Content-based 3D Model Retrieval
SMI '06 Proceedings of the IEEE International Conference on Shape Modeling and Applications 2006
Data-Driven Grasp Synthesis Using Shape Matching and Task-Based Pruning
IEEE Transactions on Visualization and Computer Graphics
Robotic Grasping of Novel Objects using Vision
International Journal of Robotics Research
A benchmark for 3D mesh segmentation
ACM SIGGRAPH 2009 papers
Learning object-specific grasp affordance densities
DEVLRN '09 Proceedings of the 2009 IEEE 8th International Conference on Development and Learning
Learning grasping affordances from local visual descriptors
DEVLRN '09 Proceedings of the 2009 IEEE 8th International Conference on Development and Learning
A hybrid approach for grasping 3D objects
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
Learning 3D mesh segmentation and labeling
ACM SIGGRAPH 2010 papers
Functional object class detection based on learned affordance cues
ICVS'08 Proceedings of the 6th international conference on Computer vision systems
Context based object categorization: A critical survey
Computer Vision and Image Understanding
Context-based search for 3D models
ACM SIGGRAPH Asia 2010 papers
Visual object-action recognition: Inferring object affordances from human demonstration
Computer Vision and Image Understanding
Characterizing structural relationships in scenes using graph kernels
ACM SIGGRAPH 2011 papers
Probabilistic reasoning for assembly-based 3D modeling
ACM SIGGRAPH 2011 papers
An overview of 3D object grasp synthesis algorithms
Robotics and Autonomous Systems
A 3D shape segmentation approach for robot grasping by parts
Robotics and Autonomous Systems
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
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We address the problem of automatic recognition of graspable parts in man-made 3D shapes, which exhibit high intra-class variability that cannot be captured with geometric descriptors alone. We observe that, in the presence of significant geometric and topological variations, the context of a part within a 3D shape provides important cues about its functionality. We propose to model the context as structural relationships between shape parts and use them, in addition to part geometry, as cues for identifying automatically the graspable parts. We design a set of spatial relationships that can be extracted from general shapes. Then, we propose a new similarity measure that captures a part context and enables better recognition of graspable parts. We use this property to design a classifier that learns the semantics of a shape part. We demonstrate that our approach outperforms the state-of-the-art approaches that are purely geometric-based.