Papers
10
Total Citations
451
H-Index
4
About
Yinlong Liu is a leading researcher in computer vision and robotics, with a primary focus on small object detection, point cloud registration, and robust pose estimation. His most impactful work, “A Survey of the Four Pillars for Small Object Detection” (2020, 330 citations), systematically addresses the persistent challenge of detecting small objects in images—a critical bottleneck for autonomous systems—by synthesizing advances in multiscale representation, contextual information, super-resolution, and region proposal techniques. Liu has also made foundational contributions to geometric perception, developing efficient and globally optimal algorithms for point cloud registration and camera orientation estimation. His 2018 paper on rotation-invariant features for global point cloud registration (81 citations) introduced a deterministic translation search method that significantly improves robustness in large-scale structural scenes. More recently, Liu has advanced globally optimal consensus maximization for relative pose estimation under planar motion and known gravity constraints, directly benefiting visual odometry and SLAM for mobile robots. His work on line-based registration and semantic-fusion pose estimation (SF-Pose) further underscores his commitment to practical, real-world robotic perception. With over 450 total citations and a growing portfolio of high-impact publications, Liu’s research bridges theoretical rigor and applied robotics, making him a key figure in enabling reliable vision systems for autonomous navigation and human-robot interaction.
Research Focus
Key Achievements
Top Papers
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- 3Fast and deterministic (3+1)DOF point set registration with gravity prior19 citations · 2023
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