Neural Surface

Neural surfaces represent 3D shapes using neural networks, aiming to achieve accurate geometry reconstruction and high-fidelity rendering, surpassing traditional methods. Current research focuses on improving model efficiency and accuracy through architectures like signed distance functions (SDFs), neural radiance fields (NeRFs), and various neural implicit surface representations, often incorporating techniques like positional encoding and attention mechanisms to handle sparse or noisy data. This field is significant for applications ranging from 3D modeling and computer graphics to robotics and medical imaging, enabling more accurate and efficient processing of complex 3D data. The development of faster, more robust, and topologically flexible neural surface representations is a key ongoing objective.