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About

Fangjinhua Wang is a versatile researcher whose work spans robotics, computer vision, and 3D scene understanding. Beginning with foundational contributions to human-robot collaboration, Wang's 2018 paper on flexible robot skin sensors — now cited 82 times — addressed critical safety challenges in industrial environments where humans and robots share physical workspaces, demonstrating an early commitment to intuitive and safe human-robot interaction. Wang's research has since evolved toward the frontier of 3D visual perception and reconstruction. His comprehensive survey on learning-based Multi-View Stereo (MVS) has established itself as a valuable reference for the community, synthesizing advances in deep learning-driven 3D reconstruction that underpin technologies in AR/VR, autonomous driving, and robotics. More recently, Wang has pushed into semantic scene understanding with the introduction of functional 3D scene graphs — a novel framework that goes beyond spatial object relationships to capture interactive and functional properties of real-world indoor environments from RGB-D imagery. Collectively, Wang's body of work reflects a trajectory from physical human-robot interfaces toward intelligent, semantics-aware 3D perception systems, positioning him as a contributor bridging robotics safety and modern scene understanding research.