Intrinsic Neural Field

Intrinsic neural fields represent a novel approach to function approximation and data representation on manifolds, aiming to leverage the underlying geometric structure for improved efficiency and robustness. Current research focuses on developing optimal network architectures with minimal neuron counts and exploring the equivalence of different coupling mechanisms in spiking neural networks, as well as applying these fields to diverse tasks like point cloud matching, photometric stereo, and texture reconstruction. This approach offers advantages in handling high-dimensional data and complex geometries, leading to improved performance in various computer vision and machine learning applications.