The moment a panoramic video looks seamless, it becomes easy to forget how many different problems were solved to make it look ordinary.
The stitchEm repository opens up the Vahana VR and VideoStitch code. Its history describes the acquisition of the code after VideoStitch closed and its release as free software. I find that valuable because a stitching application contains much more than a clever way to blend overlapping images. It has to manage an entire journey from several cameras to a usable output.
The first part is geometric. Each pixel in each camera corresponds to a direction through a lens. To combine views, the application needs a model of those lenses and their placement. A fisheye image cannot be treated as a flat photograph that merely needs sliding sideways. Its projection changes how directions are distributed across the sensor.
Calibration then gives those directions a common frame of reference. It is tempting to judge calibration using one attractive panorama, but distance matters. Cameras occupy different positions. A nearby object can shift considerably between their views while a distant wall lines up well. A rig can therefore look correctly calibrated in the background and still produce a double image of a hand close to the lenses.
Blending deals with another problem. Two cameras may report slightly different brightness or color for the same patch of scene. A hard cut between them exposes the difference. A broad blend can hide it, but may also make a misaligned moving object appear translucent. Choosing a seam is partly about choosing where those disagreements will be least objectionable.
For live video, timing belongs beside geometry. A perfect geometric map cannot align a person who has already moved between the two captured frames. The stitcher needs frames that refer to approximately the same moment. Buffers help absorb uneven delivery, at the cost of additional delay.
That delay has to be considered across the whole pipeline. Capture, transfer, decoding, warping, blending and encoding all contribute. Measuring only the GPU stitching pass gives a narrow answer. A live performer cares about the interval between doing something and seeing it reach the audience.
This is how I would approach an unfamiliar stitcher: follow one frame through the system before reading every algorithm. Where does it enter? How is its time represented? Which image format does each stage expect? Where is memory copied? When does the output become an encoded stream?
Open source makes that investigation possible without guessing from the interface. It also lets smaller components be studied on their own. A projection transform or calibration routine can teach something useful even if rebuilding the complete application is impractical.
The lasting interest is the separation of responsibilities. A stitching system is easier to reason about when a bad seam can be traced to geometry, exposure, movement or timing, instead of being treated as one undifferentiated "stitching problem."