Scene Forecasting

Scene forecasting aims to predict future states of a scene, such as the movement of objects or the evolution of a 3D environment, based on past observations. Current research heavily utilizes deep learning, employing architectures like graph neural networks (GNNs) to model relationships between objects and neural radiance fields (NeRFs) to represent and predict 3D scenes, often incorporating attention mechanisms for improved performance. This field is crucial for applications like autonomous driving, robotics, and virtual/augmented reality, where accurate prediction of future scene dynamics is essential for safe and efficient operation. The development of robust and computationally efficient scene forecasting models is driving significant advancements in these areas.