Benwen Chen
Papers
1
Total Citations
12
H-Index
1
About
Benwen Chen is a researcher in robotics and computer vision, with a focus on 3D semantic mapping for autonomous systems operating in unknown indoor environments. His key contributions lie in developing incremental, instance-oriented approaches that enable robots to build detailed semantic maps from RGB-D camera data in real time. His most-cited work, "Incremental Instance-Oriented 3D Semantic Mapping via RGB-D Cameras for Unknown Indoor Scene" (2020, 12 citations), addresses the critical challenge of scene parsing for human-robot interaction, treating the RGB-D camera as the robot's "eye" to construct rich, instance-level 3D maps from multiview images. This work is foundational for robots navigating and interacting in unfamiliar indoor spaces, bridging the gap between raw sensor data and actionable semantic understanding. Chen's research is particularly notable for its focus on incremental learning, allowing robots to update their maps continuously without full reprocessing—a key requirement for real-world deployment. His contributions advance the field of robotic perception, with implications for service robots, autonomous navigation, and assistive technologies.
Research Focus
Key Achievements
Top Papers
- 1