Recognition by functional parts
Computer Vision and Image Understanding - Special issue of funtion-based vision
Topology matching for fully automatic similarity estimation of 3D shapes
Proceedings of the 28th annual conference on Computer graphics and interactive techniques
Augmented Reeb Graphs for Content-Based Retrieval of 3D Mesh Models
SMI '04 Proceedings of the Shape Modeling International 2004
Detection and Evaluation of Grasping Positions for Autonomous Agents
CW '05 Proceedings of the 2005 International Conference on Cyberworlds
Data-Driven Grasp Synthesis Using Shape Matching and Task-Based Pruning
IEEE Transactions on Visualization and Computer Graphics
Reeb graphs for shape analysis and applications
Theoretical Computer Science
Structural Shape Prototypes for the Automatic Classification of 3D Objects
IEEE Computer Graphics and Applications
3D Mesh decomposition using Reeb graphs
Image and Vision Computing
Learning grasping affordances from local visual descriptors
DEVLRN '09 Proceedings of the 2009 IEEE 8th International Conference on Development and Learning
A Path Following Algorithm for the Graph Matching Problem
IEEE Transactions on Pattern Analysis and Machine Intelligence
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
A hybrid approach for grasping 3D objects
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
Interactive teaching of task-oriented robot grasps
Robotics and Autonomous Systems
Learning 3D mesh segmentation and labeling
ACM SIGGRAPH 2010 papers
Part analogies in sets of objects
EG 3DOR'08 Proceedings of the 1st Eurographics conference on 3D Object Retrieval
Graspable parts recognition in man-made 3d shapes
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part II
ACM Transactions on Graphics (TOG)
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Neuro-psychological findings have shown that human perception of objects is based on part decomposition. Most objects are made of multiple parts which are likely to be the entities actually involved in grasp affordances. Therefore, automatic object recognition and robot grasping should take advantage from 3D shape segmentation. This paper presents an approach toward planning robot grasps across similar objects by part correspondence. The novelty of the method lies in the topological decomposition of objects that enables high-level semantic grasp planning. In particular, given a 3D model of an object, the representation is initially segmented by computing its Reeb graph. Then, automatic object recognition and part annotation are performed by applying a shape retrieval algorithm. After the recognition phase, queries are accepted for planning grasps on individual parts of the object. Finally, a robot grasp planner is invoked for finding stable grasps on the selected part of the object. Grasps are evaluated according to a widely used quality measure. Experiments performed in a simulated environment on a reasonably large dataset show the potential of topological segmentation to highlight candidate parts suitable for grasping.