Sung-Sik Cho
Papers
1
Total Citations
6
H-Index
1
About
Sung-Sik Cho is a researcher advancing the field of computer vision, with a primary focus on efficient monocular depth estimation for mobile and robotic systems. His most notable contribution, the "Lightweight Monocular Depth Estimation via Token-Sharing Transformer" (2023), introduces a novel architecture that balances high accuracy with computational efficiency—critical for deployment on resource-constrained platforms. By leveraging a token-sharing mechanism within a transformer framework, Cho’s work enables compact models to achieve competitive depth prediction without the heavy computational overhead typical of larger networks. This innovation has garnered 6 citations in a short time, reflecting its relevance to the growing demand for real-time, low-cost depth sensing in robotics and autonomous navigation. Cho’s research directly addresses the practical challenge of integrating depth estimation into mobile systems, where size and power are limited. His work stands out for its pragmatic approach to bridging state-of-the-art performance with real-world deployability, making him a key figure in the push toward more capable and efficient robotic perception systems.
Research Focus
Key Achievements
Top Papers
- 1Lightweight Monocular Depth Estimation via Token-Sharing Transformer6 citations · 2023