Practical Kinect Stereo Calibration for the Highest Accuracy

I’ve been meaning to write up this post for a while, but I’ve been putting it off ūüôā

The release of the Kinect sensor by Microsoft spawned a plethora of research in robotics, computer vision and many other fields. Many of these attempts involved using Kinect for purposes other that what it was originally meant for! That pretty much involves anything other than gesture recognition and skeleton tracking.

Even though Kinect is a very capable device, many people (including us!) don’t get the fact that Kinect was¬†simply not designed for being used as an all-purpose depth and RGB camera. This observation is further bolstered by the fact that the Kinect was not released with a public API, and the¬†official SDK which was released much later was missing a lot of functionality critical¬†to various computer vision applications. For instance, the SDK does not provide a built-in functionality for getting color camera and depth camera calibration values, extrinsics and distortion models. Rather conveniently, the SDK designers simply chose to provide the users¬†with simple functionality, like mapping pixels from world coordinates to RGB coordinates (but no way to do a backproject an image or depth point to ray). Needless to say that in many practical computer vision applications, one needs to constantly re-calibrate the camera in order to minimize the error caused by the mis-calibration of the camera.

Nevertheless, the choice by the SDK designers is understandable: there are proprietary algorithms and methods that are implemented in the Kinect software layer and it may not always be possible to give public access to them. Also, 3rd party open source libraries (such as libfreenect) have tried to reverse-engineer many of the innerworkings of the Kinect sensor and supply the end user with a set of needed functionalities.

With all this, let’s get started! (If you are impatient, feel free to jump to the list of tips I have compiled for you at the end of this post). I have also included PDFs containing checkerboards suitable for printing on large sheets, PVC or aluminum dibond.

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Computing the Distance Between a 3D Point and a Pl√ľcker Line

In order to solve an optimization problem with the goal of reducing the distance between a bunch of 3D points and lines, I was looking for the correct way of finding the distance between 3D points and a Plucker line representation.

The Plucker line \(L\) passing through two lines \(A\) and \(B\) is defined as \(L = AB^T РBA^T\) (for more details refer to [1]). After a lot of looking, I found that there is a simple method for finding this distance in [2]. A direct quote from the paper:


A Plucker line \(L = (n, m)\) is described by a unit vector \(n\) and a
moment \(m\). This line representation allows to conveniently determine
the distance of a 3D point \(X\) to the line

$$d(X, L) = ||X \times n – m||_2$$

where \(\times\) denotes a cross product.


[1] Hartley, Richard, and Andrew Zisserman. Multiple view geometry in computer vision. Cambridge university press, 2003.

[2] Brox, Thomas, et al. “Combined region and motion-based 3D tracking of rigid and articulated objects.” IEEE Transactions on Pattern Analysis and Machine Intelligence 32.3 (2010): 402-415.