Implicit Neural Rendering

Implicit neural rendering uses neural networks to represent 3D scenes as continuous functions, enabling novel view synthesis and other applications. Current research focuses on improving the efficiency and realism of these representations, exploring architectures like signed distance functions (SDFs), occupancy fields, and Gaussian splatting, often combined with techniques for handling challenging scenarios like specular reflections. This approach offers significant advantages in areas such as autonomous driving simulation, high-fidelity human rendering for VR/AR, and efficient 3D reconstruction from images, by generating high-quality synthetic data and enabling real-time manipulation of 3D models.