Neural Implicit

Neural implicit representations are revolutionizing 3D scene modeling and understanding by representing objects and environments as continuous functions learned by neural networks, rather than discrete meshes or point clouds. Current research focuses on improving the accuracy and efficiency of these representations, particularly using signed distance fields (SDFs) and neural radiance fields (NeRFs), often within frameworks for simultaneous localization and mapping (SLAM), object reconstruction, and robotic manipulation. This approach offers compact, adaptable representations suitable for various applications, including robotics, computer vision, and scientific visualization, enabling tasks like high-resolution image segmentation, accurate 3D mapping in large-scale environments, and robust object tracking and manipulation planning directly from visual input.