- Deep Reflectance Maps
- 29th IEEE Conference on Computer Vision and Pattern Recognition
- Book/source title
- Proceedings 29th IEEE Conference on Computer Vision and Pattern Recognition : CVPR 2016
- Book/source subtitle
- 26 June-1 July 2016, Las Vegas, Nevada
- Pages (from-to)
- Los Alamitos, California: IEEE Computer Society
- ISBN (electronic)
- Document type
- Conference contribution
- Faculty of Science (FNWI)
- Informatics Institute (IVI)
Undoing the image formation process and therefore decomposing appearance into its intrinsic properties is a challenging task due to the under-constrained nature of this inverse problem. While significant progress has been made on inferring shape, materials and illumination from images only, progress in an unconstrained setting is still limited. We propose a convolutional neural architecture to estimate reflectance maps of specular materials in natural lighting conditions. We achieve this in an end-to-end learning formulation that directly predicts a reflectance map from the image itself. We show how to improve estimates by facilitating additional supervision in an indirect scheme that first predicts surface orientation and afterwards predicts the reflectance map by a learning-based sparse data interpolation. In order to analyze performance on this difficult task, we propose a new challenge of Specular MAterials on SHapes with complex IllumiNation (SMASHINg) using both synthetic and real images. Furthermore, we show the application of our method to a range of image editing tasks on real images.
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