Scene Prior

Scene priors are pre-trained models that encode general knowledge about 3D scenes, improving the performance of various computer vision tasks. Current research focuses on integrating these priors into neural radiance fields (NeRFs) and other implicit scene representations, often using techniques like Bayesian inference and generative models to handle uncertainty and noise in real-world data. This work aims to enhance the efficiency and generalizability of 3D scene reconstruction, object detection, and other applications by leveraging prior knowledge to overcome limitations of data scarcity and noisy inputs. The resulting improvements in accuracy and robustness have significant implications for robotics, autonomous driving, and virtual/augmented reality.