2014 CVPR CVPR 2014

Better Shading for Better Shape Recovery

Abstract

The basic idea of shape from shading is to infer the shape of a surface from its shading information in a single image. Since this problem is ill-posed, a number of simplifying assumptions have been often used. However they rarely hold in practice. This paper presents a simple shading-correction algorithm that transforms the image to a new image that better satisfies the assumptions typically needed by existing algorithms, thus improving the accuracy of shape recovery. The algorithm takes advantage of some local shading measures that have been driven under these assumptions. The method is successfully evaluated on real data of human teeth with ground-truth 3D shapes.

🧭 Keyword Pioneer — shading correction
🐣 Hot Topic Early Bird — computer vision
🐝 Cross-Pollinator — Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy