人工智能
计算机科学
姿势
点云
视觉对象识别的认知神经科学
标杆管理
机器人学
对象(语法)
三维单目标识别
班级(哲学)
机器学习
RGB颜色模型
比例(比率)
计算机辅助设计
计算机视觉
模式识别(心理学)
机器人
工程类
工程制图
业务
营销
物理
量子力学
作者
Walter Wohlkinger,Aitor Aldóma,Radu Bogdan Rusu,Markus Vincze
标识
DOI:10.1109/icra.2012.6225116
摘要
3D object and object class recognition gained momentum with the arrival of low-cost RGB-D sensors and enables robotics tasks not feasible years ago. Scaling object class recognition to hundreds of classes still requires extensive time and many objects for learning. To overcome the training issue, we introduce a methodology for learning 3D descriptors from synthetic CAD-models and classification of never-before-seen objects at the first glance, where classification rates and speed are suited for robotics tasks. We provide this in 3DNet (3d-net.org), a free resource for object class recognition and 6DOF pose estimation from point cloud data. 3DNet provides a large-scale hierarchical CAD-model databases with increasing numbers of classes and difficulty with 10, 50, 100 and 200 object classes together with evaluation datasets that contain thousands of scenes captured with a RGB-D sensor. 3DNet further provides an open-source framework based on the Point Cloud Library (PCL) for testing new descriptors and benchmarking of state-of-the-art descriptors together with pose estimation procedures to enable robotics tasks such as search and grasping.
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