A while ago, I started working on a dataset I captured a few years ago with a Microsoft Kinect One.

I immediately realized the data looked much cleaner than the newer datasets I created with my Intel RealSense D435.

I had already noticed that, above a certain distance, the depth data was full of craters. I already knew the error is proportional to the squared distance, but for me, it was much bigger than expected. Therefore, I calibrated the sensors and now I stay closer to my targets during the acquisitions.

But for the last dataset I captured, I tried another strategy: I decided to save also the raw IR footage to process it offline.

Stereo vision

RealSense cameras are RGBD sensors: they provide simultaneously a color (RGB) and depth (D) stream.

There are several types of techniques to measure depth. For example, the original Kinect for the Xbox 360 uses “structured light”, and the Kinect One included a time-of-flight camera.

The RealSense D400 series is based on stereo vision, which works by matching the same point in frames captured by two different cameras. There is a relation between the displacement of this point (disparity), the relative position of the two cameras, and the depth. … [Leggi il resto]

Recently, I started experimenting with stereo vision.

It is a technique to produce depth maps using images captured by close positions. Then, with these maps, it is possible to create 3D representations.

The core of this workflow is the matching algorithm, which takes pairs of post-processed images and creates a “disparity” map. The disparity is the distance between a point in the two images. Depth and disparity are inverses, so it is easy to switch from one to the other.

OpenCV contains some stereo matching algorithms, but they produced a lot of noise. So I looked for another library, and I found libelas.

It is a GPLv3 C++/MATLAB library with many parameters to tune, but I could not find a Python version. My options were to switch to C++ or to port it by myself. I chose the latter, hoping that also others can benefit from it 🙂️.

Long story short, I published my first package on PyPI: PyElas.

How to use it

You can install it using pip. Then you just have to do this: … [Leggi il resto]