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Can a depth map be transformed into a 3D point cloud?
A lot of devices, from stereoscopic cameras to 3D sensors can provide distance information about the scene they’re capturing in the form of a depth map. To obtain meaningful data from such imagery it is often necessary to transform these depth maps into a 3D point cloud.
How are depth maps stored in 3D camera?
Consider the following schematic representation of the capture of a depth sample with a 3D camera. The distance (orthogonal to the axis) from the object (a red dot in the figure) to the camera’s sensor is the one that is stored as a pixel value in the depth map.
What can you do with a depth map?
According to the Wikipedia article about it: A depth map is an image or image channel that contains information relating to the distance of the surfaces of scene objects from a viewpoint. This information can be employed in a wide variety of applications in the fields of image processing, mapping, navigation and many others.
How is the distance of an object calculated in a depth map?
The distance (orthogonal to the axis) from the object (a red dot in the figure) to the camera’s sensor is the one that is stored as a pixel value in the depth map. Obtaining the x coordinate of said sample means to calculate the horizontal distance between the object and the center of the sensor. This can be done with:
How to convert a depth map into a 3D scene?
Since it is an upper triangular matrix there is an easy analytical solution: Eq 7: Inverse o f K with all components. Now you have all the tools at hand to convert a depth map or RGBD image into a 3D scene where each pixel represents one point (Fig. 3).
How to convert a RGBD image to point cloud?
In the code (cₓ, cᵧ) is the centre of the camera sensor. Note the constant pxToMetre, a camera property, which you can determine if the focal length is known both in units of meters and in pixels. Even without it, the picture is accurately represented in 3D up to a scale factor. Of course there is a more general way to do all this.
How to create a point cloud from multiple images?
VisualSFM is an application that allows 3D reconstruction. You can get a point cloud from multiple 2D images. This video shows how to extract multiple images from a short clip of a tree and then use VisualSFM to create a point cloud. Thanks for contributing an answer to Stack Overflow!
Are there missing points in the point cloud?
Now depending on what you have done with the point cloud the points might not map exactly to image matrix pixel center points (or top left corner for some applications) or you might be missing points -> NaN/0 value pixels.
Is there way to make point cloud unstructured?
I don’t know about OpenCV methods, but in case you do something that makes your point cloud unstructured your process could be something like this