Shiqin Yan

Papers

Total Citations

452

H-Index

About

Shiqin Yan is a leading researcher at the forefront of visual computing and machine learning, whose work has fundamentally advanced the paradigm of neural fields. Her primary research focuses on leveraging coordinate-based neural networks to parameterize and model complex physical properties of scenes and objects across both space and time. Yan’s most significant contribution is her comprehensive survey, “Neural Fields in Visual Computing and Beyond,” which has garnered over 447 citations and serves as a definitive resource for the field. This seminal work systematically maps the rapid evolution of neural fields, highlighting their successful application in 3D reconstruction, novel view synthesis, and dynamic scene modeling. By synthesizing a fragmented landscape of methods, Yan has not only provided a crucial taxonomy for researchers but also illuminated the path forward for integrating neural representations into mainstream computer graphics and vision. Her work has become an essential reference, shaping how the community understands and develops these powerful tools, and establishing her as a key architect in the ongoing transformation of visual computing.