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About

Francis Engelmann is a leading researcher at the intersection of 3D computer vision, robotics, and scene understanding. His work focuses on enabling machines to perceive, segment, and interact with complex 3D environments—from indoor spaces to real-world scenes. Engelmann’s major contributions include pioneering the task of joint 3D human semantic and instance segmentation in point clouds, which is critical for human-centered robotics and AR/VR applications. His paper on this topic, published in 2023, has already garnered 19 citations. He also introduced ICGNet, a unified approach for instance-centric robotic grasping in cluttered environments, earning 9 citations. More recently, Engelmann has advanced the field with open-vocabulary functional 3D scene graphs, which capture not only spatial but also functional relationships between objects—a key step toward intelligent, context-aware robots. His work on reverse engineering CAD models from images (Img2CAD) and online 3D semantic reconstruction (ALSTER) further demonstrates his versatility and impact. With over 40 citations across his top papers, Engelmann is shaping the future of embodied AI, making his research essential reading for anyone working in 3D perception, robotics, or interactive scene understanding.