Recognizing Objects in Range Data Using Regional Point Descriptors
Recognizing Objects in Range Data Using Regional Point Descriptors
复制标题
DOI：
10.1007/978-3-540-24672-5_18
复制
发表时间：
2004-05
期刊：
影响因子：
--
通讯作者：
Andrea Frome;Daniel F. Huber;R. Kolluri;Thomas Bülow;Jitendra Malik
中科院分区：
文献类型：
--
作者：
Andrea Frome;Daniel F. Huber;R. Kolluri;Thomas Bülow;Jitendra Malik
在噪声和杂波背景下识别三维物体是三维计算机视觉中的一个具有挑战性的问题。在过去的研究中已经成功的一种方法是区域形状描述符。在本文中，我们介绍了两个新的区域形状描述符：三维形状上下文和谐波形状上下文。我们使用56辆汽车的数据库评估了这些描述符在场景范围扫描中识别车辆的任务的性能。我们比较两个新的描述符现有的描述符，自旋图像，显示基于形状上下文的描述符有较高的识别率在嘈杂的场景和3D形状上下文优于其他杂乱的场景。
Recognition of three dimensional (3D) objects in noisy and cluttered scenes is a challenging problem in 3D computer vision. One approach that has been successful in past research is the regional shape descriptor. In this paper, we introduce two new regional shape descriptors: 3D shape contexts and harmonic shape contexts. We evaluate the performance of these descriptors on the task of recognizing vehicles in range scans of scenes using a database of 56 cars. We compare the two novel descriptors to an existing descriptor, the spin image, showing that the shape context based descriptors have a higher recognition rate on noisy scenes and that 3D shape contexts outperform the others on cluttered scenes.