CAREER: Computational Passive 3D Imaging

A passive three-dimensional (3D) imager estimates the distance of objects from photographs captured without emitting light into the environment. Compared with active 3D technologies such as Light Detection and Ranging (LiDAR), passive 3D imaging offers important advantages in covertness, energy efficiency, and hardware simplicity, making it particularly attractive for applications in national defense, scientific exploration, robotics, and wearable devices. Despite substantial progress in both hardware and software, existing passive 3D imagers remain inherently limited by a short operating range, high computational cost, poor performance in low-light conditions, and difficulty integrating additional imaging functions. This project will develop a new family of passive 3D imaging solutions that overcome these limitations. Collectively termed as Computational Passive 3D Imaging, these solutions perform specialized computations directly on naturally available environmental light using coordinated optics and algorithms. Preliminary results demonstrate clear advances in range, efficiency, low-light robustness, and integrability. Once fully developed, these technologies are expected to transform 3D perception for wearable systems, robots, drones, autonomous vehicles, underwater platforms, and space exploration systems. The project will also advance engineering education by creating and disseminating learning activities themed around cameras for students from middle school through the graduate level, strengthening their experimental skills essential to the next-generation workforce. This project will introduce a set of novel imaging modalities that substantially extend the limits of passive 3D imaging in range, power consumption, low-light robustness, and multifunctionality. These modalities integrate advanced optical elements, including metasurfaces, microelectromechanical systems, and programmable optics, with both physics-based and learning-based algorithms. They perform specialized modulations on the scene’s plenoptic function during capture and complementary computations after measurement. The project will investigate the mathematical models underlying these imaging modalities, the hardware elements, and the algorithms. It will also develop simulation frameworks and experimental prototypes to analyze and explore the empirical performance of these new imaging platforms. The theoretical and practical outcomes should enable the design of advanced computational imaging systems beyond the scope of this project. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. NSF Award ID: 2544069 | Program: 01003031DB NSF RESEARCH & RELATED ACTIVIT,01002930DB NSF RESEARCH & RELATED ACTIVIT,01002627DB NSF RESEARCH & RELATED ACTIVIT | Principal Investigator: Qi Guo | Institution: Purdue University, WEST LAFAYETTE, IN | Award Amount: $359,934 View on NSF Award Search: https://www.nsf.gov/awardsearch/show-award/?AWD_ID=2544069 View on Research.gov: https://www.research.gov/awardapi-service/v1/awards/2544069.html