From shading to local shape

Ying Xiong, Ayan Chakrabarti, Ronen Basri, Steven J. Gortler, David W. Jacobs, Todd Zickler

Research output: Contribution to journalArticlepeer-review

Abstract

We develop a framework for extracting a concise representation of the shape information available from diffuse shading in a small image patch. This produces a mid-level scene descriptor, comprised of local shape distributions that are inferred separately at every image patch across multiple scales. The framework is based on a quadratic representation of local shape that, in the absence of noise, has guarantees on recovering accurate local shape and lighting. And when noise is present, the inferred local shape distributions provide useful shape information without over-committing to any particular image explanation. These local shape distributions naturally encode the fact that some smooth diffuse regions are more informative than others, and they enable efficient and robust reconstruction of object-scale shape. Experimental results show that this approach to surface reconstruction compares well against the state-of-art on both synthetic images and captured photographs.

Original languageEnglish
Article number6866216
Pages (from-to)67-79
Number of pages13
JournalIEEE Transactions on Pattern Analysis and Machine Intelligence
Volume37
Issue number1
Early online date25 Jul 2014
DOIs
StatePublished - Jan 2015

All Science Journal Classification (ASJC) codes

  • Software
  • Artificial Intelligence
  • Applied Mathematics
  • Computer Vision and Pattern Recognition
  • Computational Theory and Mathematics

Fingerprint

Dive into the research topics of 'From shading to local shape'. Together they form a unique fingerprint.

Cite this