Generalizable Neural Field

Generalizable neural fields represent 3D scenes and objects as continuous functions learned by neural networks, aiming to overcome limitations of discrete representations and enable efficient, scalable scene modeling. Current research focuses on developing architectures that generalize across different scenes and objects, often employing point-based representations and incorporating scene priors or semantic information to improve robustness and efficiency. This approach holds significant promise for applications in computer vision, robotics, and wireless communication, offering improvements in 3D reconstruction, novel view synthesis, and real-time scene understanding for tasks like mobile manipulation and radio propagation modeling.