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VFH-Color and Deep Belief Network for 3D Point Cloud Recognition

机译:用于3D点云识别的VFH-Color和Deep Belief网络

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With the invention of Microsoft Kinect sensor, 3D object recognition has become an important task in computer vision research in recent years. The Viewpoint Feature Histogram (VFH) is a Point Cloud Library (PCL) descriptor that encodes only geometry and viewpoint of 3D point cloud data. In this paper, we propose a new approach to representing and learning 3D point cloud classes. First, we develop a new descriptor called VFH-Color that combines the original version of VFH descriptor with the color quantization histogram, thus adding the appearance information that would improve the recognition rate. Then, we use those features for training deep learning algorithm called Deep Belief Network (DBN). We have also tested our approach on Washington RGBD dataset and have obtained highly promising results.
机译:随着Microsoft Kinect传感器的发明,近年来3D对象识别已成为计算机视觉研究中的重要任务。视点特征直方图(VFH)是点云库(PCL)描述符,仅对3D点云数据的几何形状和视点进行编码。在本文中,我们提出了一种表示和学习3D点云类的新方法。首先,我们开发了一个称为VFH-Color的新描述符,该描述符将VFH描述符的原始版本与颜色量化直方图相结合,从而添加了可以提高识别率的外观信息。然后,我们使用这些功能来训练称为深度信念网络(DBN)的深度学习算法。我们还在华盛顿RGBD数据集上测试了我们的方法,并获得了很有希望的结果。

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