Numair Khan

Papers

Total Citations

452

H-Index

About

Numair Khan is a leading researcher at the intersection of computer graphics, computer vision, and machine learning, whose work has fundamentally shaped the emerging field of neural fields. His primary research focuses on developing coordinate-based neural representations to model complex visual phenomena, enabling breakthroughs in 3D scene reconstruction, novel view synthesis, and physically based rendering. Khan’s most influential contribution is his landmark 2022 survey, “Neural Fields in Visual Computing and Beyond,” which has amassed over 447 citations, serving as the definitive roadmap for the field. This comprehensive work unified disparate approaches, systematically categorizing methods that parameterize scene properties across space and time, and has become essential reading for researchers and students alike. By synthesizing advances in neural rendering, geometry processing, and dynamic scene modeling, Khan’s work has accelerated the adoption of neural fields in applications ranging from virtual production to scientific visualization. His research continues to push boundaries, exploring how these representations can bridge the gap between traditional computer graphics and modern deep learning, making him a pivotal figure in the ongoing transformation of visual computing.