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About

Baoquan Chen is a pioneering researcher at the intersection of 3D computer vision, autonomous robotics, and scene reconstruction. His work has fundamentally advanced how intelligent systems perceive, reconstruct, and interact with complex three-dimensional environments. Chen is perhaps best recognized for his groundbreaking contributions to autonomous scanning, where his quality-driven and Poisson-guided frameworks redefined scan planning by prioritizing reconstruction fidelity over mere surface coverage — work that has garnered over 78 citations. His 2019 multi-robot collaborative dense scene reconstruction system, his most-cited contribution with 109 citations, demonstrated how coordinated robotic agents could efficiently map unknown indoor environments, setting a new benchmark in the field. Beyond reconstruction, Chen has explored attention-driven depth acquisition, generative 3D part assembly through dynamic graph learning, and robust camera relocalization in dynamic environments — reflecting a broad yet cohesive research vision. His leadership in the IROS 2019 Lifelong Robotic Vision Challenge further underscores his commitment to advancing continual learning in robotics. With a body of work spanning autonomous exploration, neural scene understanding, and multi-robot coordination, Chen's research continues to shape the future of intelligent 3D perception and robotic autonomy.