Ray Sampling

Ray sampling in neural radiance fields (NeRFs) aims to efficiently render realistic 3D scenes from multiple images by strategically selecting points along camera rays for processing. Current research focuses on developing novel sampling strategies, including projection-aware and object-centric approaches, to reduce computational cost while maintaining or improving image quality. These advancements leverage neural networks to predict optimal sample locations, leading to faster inference times and improved geometric accuracy compared to uniform sampling. This work has significant implications for applications requiring real-time or high-fidelity 3D scene rendering, such as virtual reality and augmented reality.