A new paradigm for recognizing 3-D objects from range data
A new paradigm for recognizing 3-D objects from range data
复制标题
从范围数据中识别 3D 对象的新范例
DOI：
10.1109/iccv.2003.1238475
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发表时间：
2003
期刊：
影响因子：
--
通讯作者：
M. Meilă
中科院分区：
文献类型：
--
作者：
S. Ruiz;L. Shapiro;M. Meilă
从距离数据中识别3D物体的大多数工作都使用了对齐验证方法，其中特定的3D物体与场景中相同物体的精确实例相匹配。这种方法已经成功地应用于工业机器视觉，但它不能处理识别相似物体类别的复杂性。本文通过提出和测试一种基于组件的方法来完成这一任务，该方法包括三个主要组成部分：1)一种从表面形状信息中学习和提取形状类组件的新方法；2)一种新的形状表示，称为符号表面签名，它总结了组件之间的几何关系；3)由分类器层次结构形成的形状类的抽象表示，这些分类器从示例中学习对象类部件及其空间关系。
Most of the work on 3D object recognition from range data has used an alignment-verification approach in which a specific 3D object is matched to an exact instance of the same object in a scene. This approach has been successfully used in industrial machine vision, but it is not capable of dealing with the complexities of recognizing classes of similar objects. This paper undertakes this task by proposing and testing a component-based methodology encompassing three main ingredients: 1) a new way of learning and extracting shape-class components from surface shape information; 2) a new shape representation called a symbolic surface signature that summarizes the geometric relationships among components; and 3) an abstract representation of shape classes formed by a hierarchy of classifiers that learn object-class parts and their spatial relationships from examples.