Lin Yen-Chen

Papers

Total Citations

H-Index

About

Lin Yen-Chen is a leading researcher in computer vision and robotics, with a focus on 3D scene understanding and neural rendering. His most influential work, "CLA-NeRF: Category-Level Articulated Neural Radiance Field" (2022, 33 citations), introduces a groundbreaking approach to modeling articulated objects—such as cabinets or scissors—without relying on CAD models or depth data. This innovation enables simultaneous view synthesis, part segmentation, and articulated pose estimation from only RGB images, advancing the ability to interact with and reconstruct dynamic, real-world objects. Yen-Chen’s contributions lie at the intersection of neural radiance fields and category-level generalization, pushing the boundaries of how machines perceive and manipulate articulated structures. His work has significant implications for robotics, augmented reality, and autonomous systems, where understanding object articulation is critical. With a growing citation impact, Yen-Chen is recognized for bridging the gap between 2D vision and 3D interaction, making complex articulated object modeling accessible and practical. His research continues to inspire new directions in neural representation and embodied AI.