The tactile internet can be described as the next step of the internet. It can empower people, adding physical interactions to what normally is just an audiovisual experience.
It however does need ultra-low latency.
A solution to having delays can be found in environment simulation.
This paper describes a way to find the correct shape of objects within the observed environment.
There already are AI-based approaches, but the problem is that they are not transparent in how they work.
Hence the research question in this paper: Is it possible to create a 3D point cloud completion algorithm from 2.5D data, without resorting to the use of AI?
A three-step approach was chosen: dividing the scene, reconstructing its objects, and turning everything into meshes. The focus was on the reconstruction. The basic principle used is that of symmetry.
The main conclusions are that this approach can work, but only for relatively simple objects.
And whereas the results are easier to understand, the logic-based approach is slower and less accurate than existing approaches.

The field of Tactile Internet (TI) is transporting physical interactions over vast distances as if they were near. This teleoperation method increases the quality of tele-engineering, telesurgery and many other fields. However, it requires the strict end to end latency constraints of a few ms, which is still out of reach. This thesis looks at the first step of this pipeline, the Human Machine Interface (HMI). We redesign a SenseGlove MK1, A VR haptic feedback glove, to reduce the latency to under 0.5 ms. We then use prediction to have an effective latency of less than -0.8 ms, creating the first Negative Latency Glove. This gives the rest of the TI system a larger latency budget than before.