Wenqiang Ren

Jiangnan University, Beijing Jiaotong University

Papers

Total Citations

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About

Wenqiang Ren is a researcher whose work bridges computer vision and robotics, with a particular focus on object affordance detection and robotic attitude sensing. Ren’s most cited paper, “A New Semantic Edge Aware Network for Object Affordance Detection” (2021, 9 citations), introduces a novel deep learning architecture that leverages semantic edge information to improve the accuracy of affordance detection—a critical capability for robots to understand how objects can be used or interacted with. This contribution advances the field of visual perception for autonomous systems. Earlier, Ren tackled a fundamental challenge in robotics with “Research on the attitude detection technology of the tetrahedron robot” (2017, 3 citations), proposing a multi-sensor data fusion algorithm based on Kalman filtering to solve the complex attitude detection problem unique to polyhedral robots. This work demonstrates Ren’s ability to address practical engineering constraints in non-standard robotic platforms. Together, these studies highlight Ren’s dual commitment to advancing both the theoretical understanding of visual semantics and the practical sensing capabilities of novel robotic systems, making meaningful contributions to the growing intersection of computer vision and robotics.