来自单个校准相机的 2 个连续帧的视差图。是否可以？
Stereo_match.cpp 示例 将L和R图像转换为视差和点云。我想改编这个示例来计算单个校准相机的 2 个连续帧的视差和点云。是否可以？如果这个例子不适合我的范围，那么获得我想要的东西的步骤是什么？
The stereo_match.cpp example converts L and R images into disparity and point cloud. I want to adapt this example for compute the disparity and point cloud from 2 consecutive frames of a single calibrated camera. Is it possible? If this example isn't good for my scope, what are the steps for obtain what I want?
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立体系统上的视差图用于获取深度信息 - 到场景中物体的距离。为此，您需要相机之间的距离，以便能够将视差信息转换为实际尺寸。
另一方面，如果您有来自静态摄像机的连续帧，我想您想要它们之间的差异。您可以通过光流算法获得它。与视差相同，对图像中的每个像素计算密集光流，并输出运动方向和幅度。最常见的 OF 是稀疏的 - 它们仅跟踪一组“强”或定义明确的点。
如果您有静态场景，但您移动相机，模拟立体装备中的两个相机，那么获得视差算法可能是有意义的。
Disparity map, on stereo systems, is used to obtain depth information - distance to objects in scene. For that, you need the distance between cameras, to be able to convert disparity info to real dimensions.
On the other hand, if you have consecutive frames from a static camera, I suppose you want the differences between them. You can obtain it with an optical flow algorithm. Dense optical flow is calculated for each pixel in image, in the same way as disparity, and it outputs the movement direction and magnitude. Most common OF are sparse - they track only a set of "strong", or well-defined points.
It may make sense to obtain disparity algorithms if you have a static scene, but you move the camera, simulating the two cameras in a stereo rig.
是，如果相机（或场景）正在移动
Yes if the camera (or scene) is moving
我想我们无法从单个相机计算出准确的视差图。在计算视差图时，我们基本上假设立体装备中两个图像的垂直像素坐标相同，只有水平像素坐标发生变化，但在单目图像序列中，这可能不成立，因为相机在两个连续的图像之间移动帧。
I suppose we cannot calculate an accurate disparity map from a single camera. In computing the disparity map we basically assume that the vertical pixel coordinate in both the images in a stereo rig is same, only the horizontal pixel coordinate changes, but in monocular image sequence, this may not hold true as the camera is moving between two consecutive frames.