Stereoscopic Video

Stereoscopic video aims to create immersive 3D experiences by generating or capturing realistic depth information for video content. Current research focuses on improving the quality and efficiency of 2D-to-3D video conversion, often employing deep learning models like diffusion-based methods and neural networks for tasks such as depth estimation, inpainting, and color correction. These advancements are driven by the increasing demand for high-fidelity 3D content in virtual and augmented reality applications, as well as for improved remote monitoring and robotic vision systems. The development of robust quality assessment metrics and large-scale datasets is also crucial for advancing the field.