Han Ling

Nanjing University of Science and Technology

Papers

Total Citations

H-Index

About

Han Ling is an emerging researcher specializing in computer vision and 3D scene understanding, with a particular focus on motion estimation and its applications in autonomous systems. Their most notable work centers on normalized scene flow (NSF), a sophisticated framework for estimating 3D motion from RGB video frame pairs by integrating optical flow with motion-in-depth estimation. This contribution, published in 2022 and titled "Scale-flow," addresses a fundamental challenge in visual perception: accurately interpreting dynamic environments in three dimensions. The approach demonstrates significant practical value for action prediction and autonomous robot navigation, offering computational and methodological advantages over prior methods. With 6 citations since its publication, the work has begun attracting attention within the computer vision community, reflecting its relevance to rapidly growing fields such as robotics, self-driving vehicles, and intelligent surveillance systems. Han Ling's research sits at the intersection of deep learning and geometric vision, tackling real-world problems that demand both theoretical rigor and practical applicability. As autonomous systems continue to advance, contributions like theirs in motion estimation and scene understanding are poised to become increasingly influential in shaping next-generation perception pipelines.