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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Barron, J.T. Malik, J. |
| Copyright Year | 2013 |
| Description | Author affiliation: UC Berkeley, Berkeley, CA, USA (Barron, J.T.; Malik, J.) |
| Abstract | In this paper we extend the “shape, illumination and reflectance from shading” (SIRFS) model [3, 4], which recovers intrinsic scene properties from a single image. Though SIRFS performs well on images of segmented objects, it performs poorly on images of natural scenes, which contain occlusion and spatially-varying illumination. We therefore present Scene-SIRFS, a generalization of SIRFS in which we have a mixture of shapes and a mixture of illuminations, and those mixture components are embedded in a “soft” segmentation of the input image. We additionally use the noisy depth maps provided by RGB-D sensors (in this case, the Kinect) to improve shape estimation. Our model takes as input a single RGB-D image and produces as output an improved depth map, a set of surface normals, a reflectance image, a shading image, and a spatially varying model of illumination. The output of our model can be used for graphics applications, or for any application involving RGB-D images. |
| Starting Page | 17 |
| Ending Page | 24 |
| File Size | 1547281 |
| Page Count | 8 |
| File Format | |
| ISBN | 9780769549897 |
| ISSN | 10636919 |
| DOI | 10.1109/CVPR.2013.10 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2013-06-23 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Shape Lighting Image segmentation Sensors Vectors Noise Computational modeling |
| Content Type | Text |
| Resource Type | Article |
| Subject | Computer Vision and Pattern Recognition Software |
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