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About

Hajimu Kawakami is a researcher in computer vision and robotics, with a primary focus on shape-from-shading (SfS) and motion estimation. His major contribution is the development of a versatile neural network method for recovering 3D shape from 2D images, a fundamental problem in visual perception. Kawakami introduced the concept of "model inclusive learning," where a neural network learns to invert an image-formation model—a mathematical description of how light interacts with surfaces to produce an image. This approach allows the network to simultaneously estimate shape, reflectance parameters, and motion fields from images, making it more robust than traditional methods. His most-cited work, "Versatile neural network method for recovering shape from shading by model inclusive learning" (2011, 7 citations), lays the foundation for this technique. Subsequent papers extended the method to estimate motion fields (2009, 6 citations) and to simultaneously recover reflection parameters (2014, 2 citations). While his citation counts are modest, Kawakami’s work represents a principled integration of physics-based models with deep learning, offering a pathway to more interpretable and generalizable computer vision systems. His research is particularly valuable for students and researchers interested in the intersection of classical vision theory and modern neural network approaches.