Neural Implicit Representation

Neural implicit representations (NIRs) are a powerful technique for encoding complex data, such as 3D shapes and images, into compact, continuous functions. Current research focuses on improving NIR accuracy, efficiency, and generalization capabilities across diverse applications, often employing architectures like neural radiance fields (NeRFs) and convolutional networks to address limitations in existing methods. This approach offers significant advantages in various fields, including medical imaging, robotics, and computer vision, by enabling efficient data storage, high-fidelity reconstruction, and improved performance in tasks like anomaly detection and 3D mapping. The development of more robust and efficient NIRs is driving progress in numerous scientific and engineering domains.