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SLombardi_CVPR12.pdf
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🔗 Source: vision.ist.i.kyoto-u.ac.jp
📊 Size: 450 KB
📄 Pages: 8 pages
⬇️ Downloads: 133

Summary

Estimating reflectance and illumination from a single image is challenging when the object surface consists of multiple materials. The key difficulty lies in recovering reflectance from sparse angular samples while correctly assigning them to different materials. A novel approach is proposed, which extracts and leverages reflectance priors to constrain the possible solutions, ensuring the recovered reflectance conforms to real-world materials. A layered Markov random field formulation is used to model material regions and their spatially varying reflectance, jointly estimating material segmentation, reflectance, and the direction and strength of a single point source. The method is experimentally evaluated on synthetic and real object surfaces, demonstrating accurate segmentation and recovery of reflectance, and handling real-world materials that cannot be modeled with conventional parametric models.

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Description

Estimating reflectance and illumination from a single image is challenging when the object surface consists of multiple materials.

Technical Information

  • File Format: PDF
  • File Size: 450 KB
  • Pages: 8
  • Language: EN
  • Total Downloads: 133
  • Last Updated: 2 hours ago

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