Fringe projection profilometry shifts toward computational 3D imaging

A new review in Light: Advanced Manufacturing says fringe projection profilometry is moving beyond classic geometric 3D measurement and toward a computational model that better handles reflective, translucent and complex scenes. The authors frame artificial intelligence and computational imaging as the next step for 3D vision.

Why it matters: - Fringe projection profilometry is a core 3D sensing method used in industrial metrology, intelligent manufacturing and scientific research. - The review argues that the field now needs to move beyond simple shape measurement and toward understanding how light behaves in complex scenes. - That shift could make 3D vision more usable on reflective surfaces, translucent materials, biological tissue and other hard-to-measure targets.

What happened: - Zhoujie Wu and Qican Zhang of Sichuan University, along with Gunther Notni of the Fraunhofer Institute for Applied Optics and Precision Engineering IOF, published a review in Light: Advanced Manufacturing. - The paper summarizes more than 40 years of fringe projection profilometry development. - The review says the field is changing from traditional geometric triangulation to computational 3D imaging. - The article is identified by DOI 10.37188/lam.2026.074.

The details: - Fringe projection profilometry began in the early 1980s. - The method projects structured fringe patterns onto an object and analyzes phase information to reconstruct 3D shape. - The classic approach assumes most captured signal comes from direct surface reflections. - In real-world scenes, multiple reflections, subsurface scattering and other global illumination effects complicate the measurements. - Under those conditions, the recorded data carries both geometric information and information about light transport. - The review splits the field into three stages: the Foundation Phase from 1983 to 2006, the Booming Phase from 2007 to 2018, and the Transformative Phase from 2019 to the present. - The foundation phase established the theoretical basis of fringe projection profilometry. - The booming phase focused on improving accuracy, speed and hardware performance. - That progress helped move the technology from lab research into broader industrial use. - The transformative phase reflects growing demand for 3D sensing in extreme scales, dynamic scenes, diverse materials and difficult environments.

Between the lines: - The review’s central point is that more speed and better hardware are no longer enough. - The authors argue that the next leap in 3D vision depends on framework innovation. - Artificial intelligence is positioned as a tool for solving complex inverse problems. - Computational imaging is positioned as a way to build richer physical models of light-matter interaction. - Together, the two approaches are presented as the engine of a new computational 3D imaging framework.

What’s next: - The field is likely to focus on systems that can infer both object shape and light transport in the same measurement pipeline. - Future 3D sensing research will likely emphasize challenging scenes where conventional triangulation struggles. - The review suggests AI and computational imaging will play a larger role in the next generation of fringe projection profilometry.

The bottom line: - Fringe projection profilometry is evolving from a geometry-first measurement tool into a more intelligent 3D imaging system built to handle complex real-world optics.