Single image 3D object detection and pose estimation for grasping
| Autor: | Samarth Brahmbhatt, Matthieu Lecce, Kostas Daniilidis, Menglong Zhu, Cody J. Phillips, Mabel M. Zhang, Konstantinos G. Derpanis, Yinfei Yang |
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| Rok vydání: | 2014 |
| Předmět: |
Computer science
business.industry ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Pattern recognition 3D pose estimation Edge detection Object detection Silhouette Object-class detection Image texture Computer vision Viola–Jones object detection framework Artificial intelligence business Pose ComputingMethodologies_COMPUTERGRAPHICS Feature detection (computer vision) |
| Zdroj: | ICRA |
| Popis: | We present a novel approach for detecting objects and estimating their 3D pose in single images of cluttered scenes. Objects are given in terms of 3D models without accompanying texture cues. A deformable parts-based model is trained on clusters of silhouettes of similar poses and produces hypotheses about possible object locations at test time. Objects are simultaneously segmented and verified inside each hypothesis bounding region by selecting the set of superpixels whose collective shape matches the model silhouette. A final iteration on the 6-DOF object pose minimizes the distance between the selected image contours and the actual projection of the 3D model. We demonstrate successful grasps using our detection and pose estimate with a PR2 robot. Extensive evaluation with a novel ground truth dataset shows the considerable benefit of using shape-driven cues for detecting objects in heavily cluttered scenes. |
| Databáze: | OpenAIRE |
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