2015 | OriginalPaper | Buchkapitel
Depth Estimation for Glossy Surfaces with Light-Field Cameras
verfasst von : Michael W. Tao, Ting-Chun Wang, Jitendra Malik, Ravi Ramamoorthi
Erschienen in: Computer Vision - ECCV 2014 Workshops
Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.
Wählen Sie Textabschnitte aus um mit Künstlicher Intelligenz passenden Patente zu finden. powered by
Markieren Sie Textabschnitte, um KI-gestützt weitere passende Inhalte zu finden. powered by
Light-field cameras have now become available in both consumer and industrial applications, and recent papers have demonstrated practical algorithms for depth recovery from a passive single-shot capture. However, current light-field depth estimation methods are designed for Lambertian objects and fail or degrade for glossy or specular surfaces. Because light-field cameras have an array of micro-lenses, the captured data allows modification of both focus and perspective viewpoints. In this paper, we develop an iterative approach to use the benefits of light-field data to estimate and remove the specular component, improving the depth estimation. The approach enables light-field data depth estimation to support both specular and diffuse scenes. We present a physically-based method that estimates one or multiple light source colors. We show our method outperforms current state-of-the-art diffuse and specular separation and depth estimation algorithms in multiple real world scenarios.