Our model for disparity remapping is built on perceptual experiments.
In those paricipants had to decide whether stimulus is moving in depth or not.
Or whether its disparity corrugation is being scaled.
From that we have learnt threshold speeds for our model.
We use them to power our applications such as the gaze-driven remapping.
Here you see a static linear mapping of disparic and gaze as a white dot.
Such mapping does not adapt to a gaze location change in any way.
An alternative shown here shifts the disparity at the gaze to the screen.
That, however, leads to annoying jumps.
Hanhart et al. proposed a temporally coherent extension to improve stability.
Our method takes scaling of depth into account.
That opens much more mapping possibilities.
The model ensures that no temporal artifacts are introduced.
Here we present resulting mappings as recorded using an eye tracker.
Note that this cannot reproduce a real user experience.
First we show a static mapping.
Then our gaze adaptive mapping.
You may follow the gaze dot and note the depth enhancement.
And finally a comparison of all 4 method using a splitted screen.
Our method does not show artifacts like the immediate shift.
It also leads to bigger depth impression than alternatives.
Please refer to the paper for a user study.
Here some more results for both CG and real content.
Our transition model is also useful to reduce abrupt disparity changes...
...in movie cuts such as this one.
The disparity change is seamlessly distributed to other frames.
A comparison.
We also support an offline depth preprocessing.
Per-frame constructed mapping curves are temporally incoherent.
Our model finds new curves and seamless transitions between them.
A comparison.
Thank you.