Environment Reconstruction

Environment reconstruction aims to create digital 3D models of real-world spaces, focusing on accuracy and efficiency. Current research emphasizes leveraging diverse data sources, including visual data (photogrammetry, video), radio frequency signals, and even human-object interactions, often incorporating deep learning models like convolutional neural networks, generative adversarial networks, and neural radiance fields (NeRFs) for processing and reconstruction. These advancements are improving the quality and speed of 3D model generation, with applications ranging from virtual and augmented reality to robotics and wireless communication system design.