Recovery of Parametric Models from Range Images: The Case for Superquadrics with Global Deformations
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robot grasp synthesis algorithms: a survey
International Journal of Robotics Research
Proceedings of the sixth ACM symposium on Solid modeling and applications
3D zernike descriptors for content based shape retrieval
SM '03 Proceedings of the eighth ACM symposium on Solid modeling and applications
Reliable Recovery of Piled Box-like Objects via Parabolically Deformable Superquadrics
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Robotic Grasping of Novel Objects using Vision
International Journal of Robotics Research
An Active Vision System for Detecting, Fixating and Manipulating Objects in the Real World
International Journal of Robotics Research
Learning grasping points with shape context
Robotics and Autonomous Systems
A strategy for grasping unknown objects based on co-planarity and colour information
Robotics and Autonomous Systems
Learning to grasp unknown objects based on 3D edge information
CIRA'09 Proceedings of the 8th IEEE international conference on Computational intelligence in robotics and automation
A complete and efficient algorithm for searching 3-D form-closure grasps in the discrete domain
IEEE Transactions on Robotics
Learning Object Affordances: From Sensory--Motor Coordination to Imitation
IEEE Transactions on Robotics
SHREC'12 track: 3D mesh segmentation
EG 3DOR'12 Proceedings of the 5th Eurographics conference on 3D Object Retrieval
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In this paper, we conclude our work on shape approximation by box primitives for the goal of simple and efficient grasping. As a main product of our research, we present the BADGr toolbox for Box-based Approximation, Decomposition and Grasping of objects. The contributions of the work presented here are twofold: in terms of shape approximation, we provide an algorithm for creating a 3D box primitive representation to identify object parts from 3D point clouds. We motivate and evaluate this choice particularly towards the task of grasping. As a contribution in the field of grasping, we further provide a grasp hypothesis generation framework that utilizes the chosen box presentation in a flexible manner.