Photorealistic 3D

Photorealistic 3D reconstruction aims to create highly realistic three-dimensional models from various input sources, primarily images and videos. Current research heavily utilizes Gaussian splatting, a method that represents scenes using collections of 3D Gaussian primitives, offering advantages in speed and rendering quality compared to earlier neural radiance field (NeRF) approaches. These techniques are being refined to address challenges like efficient multi-view fusion, real-time performance, and accurate reconstruction of complex scenes, including humans and urban environments. The resulting advancements have significant implications for robotics, autonomous driving, virtual reality, and various other fields requiring accurate and visually compelling 3D models.