Semantic Communication (SC) is a novel paradigm for data transmission in 6G. However, there are several challenges posed when performing SC in 3D scenarios: 1) 3D semantic extraction; 2) Latent semantic redundancy; and 3) Uncertain channel estimation. To address these issues, we propose a Generative AI Model assisted 3D SC (GAM-3DSC) system. Firstly, we introduce a 3D Semantic Extractor (3DSE), which employs generative AI models, including Segment Anything Model (SAM) and Neural Radiance Field (NeRF), to extract key semantics from a 3D scenario based on user requirements. The extracted 3D semantics are represented as multi-perspective images of the goal-oriented 3D object. Then, we present an Adaptive Semantic Compression Model (ASCM) for encoding these multi-perspective images, in which we use a semantic encoder with two output heads to perform semantic encoding and mask redundant semantics in the latent semantic space, respectively. Next, we design a conditional Generative adversarial network and Diffusion model aided-Channel Estimation (GDCE) to estimate and refine the Channel State Information (CSI) of physical channels. Finally, simulation results demonstrate the advantages of the proposed GAM-3DSC system in effectively transmitting the goal-oriented 3D scenario.

It is a widely known fact in the tech world that drones are flown at high altitudes, and they cannot yet fly autonomously in complex environments, like dense forests. However, thanks to latest advancements in artificial intelligence and computer vision, today drones can maneuver indoors, around difficult to reach nooks, bends and even dense forests too. Well, recently drones again hit the headlines, owing to their new ability to help people, hikers lost in woods. In their paper published in the journal Nature Machine Intelligence, researchers, David Schedl, Indrajit Kurmi and Oliver Bimber, from Johannes Kepler University, share how artificial intelligence to improve thermal imaging camera searches for people lost in woods. When hikers, trekkers or commoners are lost in woods, rescue team rely on binoculars, and thermal imagers installed on camera and in chopper sensors, to find the missing.

Today drones can maneuver indoors, around difficult to reach nooks, bends and even dense forests too. Well, recently drones again hit the headlines, owing to their new ability to help people, hikers lost in woods. In their paper published in the journal Nature Machine Intelligence, researchers, David Schedl, Indrajit Kurmi and Oliver Bimber, from Johannes Kepler University, share how artificial intelligence to improve thermal imaging camera searches for people lost in woods. When hikers, trekkers or commoners are lost in woods, rescue team rely on binoculars, and thermal imagers installed on camera and in chopper sensors, to find the missing. Generally, the thermal imaging devices highlight differences in body temperature of people on the ground versus their surroundings, making them easier to spot.