Neural Field Representation

Neural field representations are implicit models that use neural networks to represent continuous signals, such as images, 3D shapes, and scenes, offering compact and efficient representations. Current research focuses on improving model architectures, including grid-based methods, point-based approaches, and hybrid models that combine their strengths, as well as optimizing training procedures for speed and efficiency. These advancements are driving progress in various applications, such as novel view synthesis, 3D reconstruction, robotic manipulation planning, and efficient compression of point cloud data, by enabling more accurate, detailed, and computationally feasible representations of complex data.