立体匹配中的视差图和视差图像之间的差异 [英] Difference between Disparity map and Disparity Image in Stereo Matching

问题描述

我是新的立体声匹配。我不明白差异的概念。请告诉我什么是视差图和视差图像，它们之间有什么区别？

解决方案

视差

视差是指立体对的左图像和右图像中的两个对应点之间的距离。如果你看下面的图像，你会看到一个标记点​​X（忽略X1，X2和X3）。通过遵循从X到OL的虚线，您可以看到与XL的左手平面的交点。同样的原则适用于右侧图像平面。

显然，这个过程涉及在左手帧中选择一个点，然后在右手图像中找到它的匹配（通常称为对应点）;

视差图/图像

如果你要对左手图像中的每个像素执行这个匹配过程，在右边框架中找到它的匹配，并计算它们之间的距离，你会得到一个图像，其中每个像素包含距离/视差值。

示例

给定左图

>

还有正确的图片

通过将左手图像中的每个像素与右手图像中的相应像素进行匹配并计算像素值之间的距离（差异），您应该其中的图片如下所示：

此底部图片称为视差图片/地图。阅读有关执行立体匹配的有用主题是更正。这将使得左右图像中的像素匹配的过程显着更快，因为搜索将是水平的。

I am new to Stereo Matching. I couldn't understand the concept of Disparity. Please tell me what is disparity map and disparity image and also What is the difference between them? Are both the terms same or different?

解决方案

Disparity

Disparity refers to the distance between two corresponding points in the left and right image of a stereo pair. If you look at the image below you see a labelled point X (ignore X1, X2 & X3). By following the dotted line from X to OL you see the intersection point with the left hand plane at XL. The same principal applies with the right-hand image plane.

If X projects to a point in the left frame XL = (u,v) and to the right frame at XR = (p,q) you can find the disparity for this point as the magnitude of the vector between (u,v) and (p,q). Obviously this process involves choosing a point in the left hand frame and then finding its match (often called the corresponding point) in the right hand image; often this is a particularly difficult task to do without making a lot of mistakes.

Disparity Map/Image

If you were to perform this matching process for every pixel in the left hand image, finding its match in the right hand frame and computing the distance between them you would end up with an image where every pixel contained the distance/disparity value for that pixel in the left image.

Example

Given a left image

And a right image

By matching every pixel in the left hand image with its corresponding pixel in the right hand image and computing the distance between the pixel values (the disparities) you should end up with images that look like this:

This bottom image is known as a disparity image/map. A useful topic to read about when performing stereo matching is rectification. This will make the process of matching pixels in the left and right image considerably faster as the search will be horizontal.

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