This project is creating a novel paradigm for computer vision, termed "reconstructive recognition", that incorporates the strongest elements of previous machine learning-based recognition efforts and the strongest elements of previous reconstruction efforts based on radiometric reasoning. The goal is to provide a new foundation for machine perception, and the potential for a transformative advance in applications of computer vision. The project seeks novel physics-based methods for recognition as well as novel learning-based methods for interpreting pixel values in terms of the physics of a scene. The agenda is structured around four aims: Aim I develops generalized reconstructive processes that unify the recovery of shape, materials, motion and illumination. Aim II focuses on supervised visual learning methods that exploit such reconstructive image representations. Aim III pursues unsupervised discovery of reconstructive representations that converge to be similar to the engineered models of Aim I. Finally, Aim IV introduces well-defined challenge problems that focus the field and serve as measurable proxies for progress in computer vision applications that have high potential impact on society.

There is a significant broader impact to this project, not least being the improvement in computer vision pedagogy that ensues from a reunification of the currently divergent recognition and reconstruction views of the field. More broadly, this project pursues critical steps toward a future where machines can see, a future that will bring changes to robotics, human-computer interfaces, security, and autonomous navigation, to name a few.

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University of California Berkeley
United States
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