3-D Data Acquisition and Shape Recognition by Multiple Views

对于三维形状的识别和三维位置的测量，视觉系统需要对场景中密集点的三维数据进行测量。一种很有前途的方法是根据三角测量原理根据两幅图像的视差来测量距离。然而，这种双目方法的一个问题是不容易找到特征点的对应关系，在本研究中，我们提出了一种新的方法来解决这一对应问题，该方法可以通过增加一个摄像机来解决几何上的对应问题，即通过三目视觉。我们制作了三目视觉系统，并在各种物体上进行了实验。从这些实验中我们发现，三眼视觉的几何约束对于寻找对应是非常强大的，但是，随着边缘点密度的增加，出现多个候选点的可能性也增加了。为了解决这个问题，我们还引入了其他性质，如局部属性的相似性和边的连续性。也就是说，当存在多个受几何约束约束的候选对象时，我们检查局部属性和边缘连续性的相似性，确定真实的对应点，并使用上述方法进行实验，发现即使在复杂场景下也可以建立正确的对应关系。

For recognition of 3-D shape and measurement of 3-D position it is important that a vision system can measure the 3-D data of dense points in the scene. One promising approach is to measure the distance on the basis of the triangulation principle from the disparity of two images. However, a problem of this binocular method is that it is not easy to find correspondence of feature points.In this research, we propose a new approach for this correspondence problem in which the correspondence problem can be solved geometrically by adding one more camera, that is, by trinocular vision. We have made the trinocular vision system and have made experiments on a variety of objects. From these experiments we have found that the geometrical constraints of the trinocular vision is very powerful for finding correspondence.However, when the density of edge points increases, chances of points which have multiple candidata points also increase. To solve this problem we also introduced other properties such as similarity of local properties and edge continuity. That is , when there are multiple candidates by geometrical constraints, we check similarity of local properties and edge continuity, and determine the true corresponding point.Using the above method, we have made experiments and have found that correct correspondence can be established even for complicated scene.