Chris Sweeney

Massachusetts Institute of Technology, Meta (Israel)

Papers

Total Citations

H-Index

About

Chris Sweeney’s research lies at the intersection of 3D scene understanding, robotic perception, and augmented reality, with a focus on enabling machines to perceive and interact with complex environments. His major contributions include developing ODAM, a pioneering system for 3D Object Detection, Association, and Mapping from posed RGB video, which advances high-level scene understanding for robotics and AR applications. Sweeney also made significant strides in sensor modeling with his work on predicting noise in depth images, providing a supervised approach that improves the fidelity of robotic simulations—critical for testing autonomous systems. Both papers have garnered 29 citations each, reflecting their growing influence in the field. His work directly addresses the challenge of bridging simulation and reality, ensuring that robotic behaviors verified in virtual environments translate reliably to the physical world. Sweeney’s research is notable for its practical impact, offering tools that enhance the robustness of perception systems in real-world settings. For students and researchers, his contributions exemplify how rigorous sensor modeling and object-level mapping can unlock new capabilities in autonomous navigation and interactive 3D applications.