Mono to Stereo

Mono-to-stereo conversion encompasses techniques for generating three-dimensional (3D) information or stereo content from single-view (monocular) inputs, such as images or audio. Current research focuses on improving the accuracy and efficiency of these conversions using various approaches, including diffusion models for generating high-fidelity stereoscopic videos and advanced neural networks (e.g., Transformers, CNNs) for stereo matching and depth estimation from images. These advancements have significant implications for fields like virtual and augmented reality, autonomous driving, and 3D modeling, enabling the creation of immersive experiences and improved scene understanding from limited sensory data.