LIME: Live Intrinsic Material Estimation

Abhimitra Meka, Maxim Maximov, Michael Zollhöfer, Hans-Peter Seidel, Christian Richardt, Christian Theobalt

Research output: Chapter or section in a book/report/conference proceedingChapter in a published conference proceeding

71 Citations (SciVal)
57 Downloads (Pure)


We present the first end-to-end approach for real-time material estimation for general object shapes with uniform material that only requires a single color image as input. In addition to Lambertian surface properties, our approach fully automatically computes the specular albedo, material shininess, and a foreground segmentation. We tackle this challenging and ill-posed inverse rendering problem using recent advances in image-to-image translation techniques based on deep convolutional encoder–decoder architectures. The underlying core representations of our approach are specular shading, diffuse shading and mirror images, which allow to learn the effective and accurate separation of diffuse and specular albedo. In addition, we propose a novel highly efficient perceptual rendering loss that mimics real-world image formation and obtains intermediate results even during run time. The estimation of material parameters at real-time frame rates enables exciting mixed-reality applications, such as seamless illumination-consistent integration of virtual objects into real-world scenes, and virtual material cloning. We demonstrate our approach in a live setup, compare it to the state of the art, and demonstrate its effectiveness through quantitative and qualitative evaluation.
Original languageEnglish
Title of host publication2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
Number of pages10
ISBN (Electronic)9781538664209
ISBN (Print)9781538664216
Publication statusPublished - 17 Dec 2018
EventInternational Conference on Computer Vision and Pattern Recognition - Salt Lake City, USA United States
Duration: 18 Jun 201822 Jun 2018

Publication series

ISSN (Electronic)2575-7075


ConferenceInternational Conference on Computer Vision and Pattern Recognition
Abbreviated titleCVPR
Country/TerritoryUSA United States
CitySalt Lake City
Internet address


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