Evaluating our method on public datasets, we show that it can successfully solve challenging situations in dynamic environments which cause state-of-the-art baseline VSLAM algorithms to fail and that it maintains performance on static scenes.
A new double depth-map representation of the human shape allows to recover 3D details from a single image. Using 2 depth maps (visible and hidden) makes representations more efficient and easier to handle.
A combination of collaborative filtering approaches and contextual information can help overcome unpredictable behavior while taking into account position and layout bias for more effective recommendation.
The acme of perfection of AR is when you can’t distinguish the virtual from the real. We experiment with AI and computer vision in our AR museum guide ARAO to make the physical experience a natural one.