By Raji Natarajan, Special to Rice News Picture a busy street during rush hour with vehicles zipping past and pedestrians rushing by. While humans can effortlessly perceive and interpret such dynamic scenes in real time, current 3D imaging technologies often struggle to create accurate representations of rapidly changing environments. A new study published in Nature Communications by researchers at Rice University and the University of Arizona introduces an innovative approach that could help machines see the world more clearly. Ashok Veeraraghavan, chair of the Department of Electrical and Computer Engineering in Rice’s George R. Brown School of Engineering and Computing, and Aniket Dashpute, a graduate student in his lab, in collaboration with associate professor Florian Willomitzer and his team at the University of Arizona’s Wyant College of Optical Sciences, developed a two-step computational imaging method that uses a laser and a high-speed camera to capture complex scenes in three dimensions with exceptional speed and accuracy. The key innovation lies in the system’s ability to transform ordinary matte surfaces into virtual screens. By using surrounding walls, furniture, clothing and other nonreflective surfaces as part of the imaging process, the technique enables accurate 3D reconstruction of scenes containing both matte and reflective objects, a long-standing challenge in computer vision. The advance could improve machine vision for applications ranging from industrial inspection and facial recognition to human sensing and autonomous vehicles. Most 3D imaging systems rely on structured light, which projects patterns onto a scene and measures how those patterns deform across object surfaces to create depth maps. While widely used, these systems can struggle with motion, challenging lighting conditions and scenes containing both matte and reflective materials. In mixed-reflectance environments, light bouncing between surfaces can distort measurements and reduce image quality. “To address these challenges, we leveraged a well-established technique in computer