Object recognition and localization via pose clustering
Computer Vision, Graphics, and Image Processing
Recovery of Parametric Models from Range Images: The Case for Superquadrics with Global Deformations
IEEE Transactions on Pattern Analysis and Machine Intelligence
Describing Complicated Objects by Implicit Polynomials
IEEE Transactions on Pattern Analysis and Machine Intelligence
Interactive learning of a multiple-attribute hash table classifier for fast object recognition
Computer Vision and Image Understanding
The Role of Model-Based Segmentation in the Recovery of Volumetric Parts From Range Data
IEEE Transactions on Pattern Analysis and Machine Intelligence
Using Spin Images for Efficient Object Recognition in Cluttered 3D Scenes
IEEE Transactions on Pattern Analysis and Machine Intelligence
Rapid Object Indexing Using Locality Sensitive Hashing and Joint 3D-Signature Space Estimation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Three-Dimensional Model-Based Object Recognition and Segmentation in Cluttered Scenes
IEEE Transactions on Pattern Analysis and Machine Intelligence
SGP '05 Proceedings of the third Eurographics symposium on Geometry processing
Superquadrics and Angle-Preserving Transformations
IEEE Computer Graphics and Applications
4-points congruent sets for robust pairwise surface registration
ACM SIGGRAPH 2008 papers
A concise and provably informative multi-scale signature based on heat diffusion
SGP '09 Proceedings of the Symposium on Geometry Processing
Model-based 3D object detection: Efficient approach using superquadrics
Machine Vision and Applications
An efficient RANSAC for 3D object recognition in noisy and occluded scenes
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part I
Low-Cost laser range scanner and fast surface registration approach
DAGM'06 Proceedings of the 28th conference on Pattern Recognition
Learning of grasp selection based on shape-templates
Autonomous Robots
Occlusion-aware multi-view reconstruction of articulated objects for manipulation
Robotics and Autonomous Systems
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In this paper, we present an efficient 3D object recognition and pose estimation approach for grasping procedures in cluttered and occluded environments. In contrast to common appearance-based approaches, we rely solely on 3D geometry information. Our method is based on a robust geometric descriptor, a hashing technique and an efficient, localized RANSAC-like sampling strategy. We assume that each object is represented by a model consisting of a set of points with corresponding surface normals. Our method simultaneously recognizes multiple model instances and estimates their pose in the scene. A variety of tests shows that the proposed method performs well on noisy, cluttered and unsegmented range scans in which only small parts of the objects are visible. The main procedure of the algorithm has a linear time complexity resulting in a high recognition speed which allows a direct integration of the method into a continuous manipulation task. The experimental validation with a seven-degree-of-freedom Cartesian impedance controlled robot shows how the method can be used for grasping objects from a complex random stack. This application demonstrates how the integration of computer vision and soft-robotics leads to a robotic system capable of acting in unstructured and occluded environments.