Papers
10
Total Citations
261
H-Index
7
About
Yihong Wu is a leading researcher in robotics and computer vision, specializing in 3D reconstruction, simultaneous localization and mapping (SLAM), and sensor calibration. Their work has significantly advanced indoor scene understanding, with a focus on high-quality 3D reconstruction using RGB-D cameras and LiDAR sensors. Wu's most cited paper (85 citations) provides a comprehensive review of indoor scene 3D reconstruction techniques, while their work on LiDAR SLAM with PCA-based feature extraction (77 citations) addresses critical challenges in robotic navigation. Their research on dynamic SLAM (31 citations) has been instrumental in enabling robots to operate effectively in changing environments. Wu has also made notable contributions to implicit neural mapping for large-scale scenes, camera calibration methods, and multi-sensor fusion techniques. Their work on the CID-SIMS dataset (10 citations) provides valuable resources for advancing semantic SLAM and 3D reconstruction research. With over 260 total citations across their publications, Wu continues to push boundaries in creating robust, efficient mapping solutions for autonomous systems, from indoor robots to autonomous driving applications.
Research Focus
Key Achievements
Top Papers
- 1
- 2A LiDAR SLAM With PCA-Based Feature Extraction and Two-Stage Matching77 citations · 2022
- 3
- 4An Easy Calibration Method for Central Catadioptric Cameras25 citations · 2007
- 5
- 6
- 7Robust and Efficient CPU-Based RGB-D Scene Reconstruction10 citations · 2018
- 8
- 9
- 10Scene-Unified Image Translation For Visual Localization3 citations · 2020