Papers
7
Total Citations
151
H-Index
5
About
Fulin Tang is a leading researcher in computer vision and robotics, specializing in 3D scene reconstruction, camera localization, and multi-sensor fusion. Their seminal work, "Image-based camera localization: an overview" (2018), with 102 citations, provides a comprehensive review of a critical task for virtual reality, augmented reality, robotics, and autonomous driving—filling a long-standing gap in the literature. Tang’s contributions extend to large-scale 3D mapping, where they proposed an implicit neural mapping approach that achieves high accuracy with compact memory usage, as seen in their 2023 paper (14 citations). They also advanced indoor robotics with the CID-SIMS dataset (10 citations), enabling enhanced SLAM and 3D reconstruction for ground wheeled robots. Tang’s innovative work on CPU-based RGB-D scene reconstruction (2018, 10 citations) addresses the challenge of tracking in textureless environments, while their recent research on CornerVINS (2025) introduces hierarchical geometric representations for accurate localization in structural environments. Additionally, Tang’s targetless LiDAR–camera extrinsic calibration method (2025) improves multimodal sensor fusion for autonomous driving. With a focus on robust, memory-efficient solutions, Tang’s work has significantly impacted both academic research and practical applications in robotics and autonomous systems.
Research Focus
Key Achievements
Top Papers
- 1Image-based camera localization: an overview102 citations · 2018
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- 4Robust and Efficient CPU-Based RGB-D Scene Reconstruction10 citations · 2018
- 5Image Based Camera Localization: an Overview9 citations · 2016
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