In the first video we’ve completely removed the perspective correction factor. Without this, as the camera moves around the scene, the tracking processes does it best to stitch “skewed” frame data into world space yet inevitably fails.
The next video takes a much larger pan but due to the flat wall surfaces being tracked that provide little depth variation, the tracking algorithm drifts upwards.
In the above videos, the top
two left window show what the camera is seeing as a colour image and as a
greyscale depth map. The bottom left
window is a 3D point cloud representation of the RGB and depth data combined
from the Kinect sensor (which would normally be correctly coloured, but the
green in this window represents point matches into the world space). The larger window in the centre of the screen
is the compiled world space. The green
wireframe box indicates the current camera position and orientation into this
world. Green indicates the points that
are paired with the individual captures from the Kinect device.
Underneath the larger 3D window
are "debug" outputs - the one on the left give internal states for
the steps within the matching process and the right one gives the camera
orientation of the current frame in terms of rotation and offset into the
global space.
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