Tian-Liang Lin

About

Tian-Liang Lin is a researcher in computer vision and robotics, with a primary focus on monocular 6D pose estimation—a critical task for enabling machines to perceive and interact with three-dimensional objects from single-camera inputs. His most cited work, "Dual Branch PnP Based Network for Monocular 6D Pose Estimation" (2023), addresses a key limitation in existing 2D-3D correspondence-based methods, which often falter in monocular settings due to depth ambiguity and occlusion. Lin’s major contribution lies in designing a dual-branch architecture that integrates a Perspective-n-Point (PnP) solver directly into the learning pipeline, allowing the network to jointly reason about geometric correspondences and pose parameters. This innovation improves robustness and accuracy in real-world scenarios where only a single RGB image is available. Although his work is early-stage, with 2 citations to date, it represents a meaningful step toward practical deployment in augmented reality, autonomous navigation, and robotic manipulation. Lin’s research sits at the intersection of deep learning and geometric vision, and his dual-branch approach offers a promising template for future work in monocular perception.

Research Focus

Key Achievements

Top Papers

1. 1
   Dual Branch PnP Based Network for Monocular 6D Pose Estimation

   2 citations · 2023

Key Collaborators

Contact & Links