From Shading to Local Shape

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

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 surface reconstruction by this approach compares well against the state-of-art on both synthetic images and captured photographs.


Ying Xiong, Ayan Chakrabarti, Ronen Basri, Steven J. Gortler, David W. Jacobs, and Todd Zickler, "From Shading to Local Shape", IEEE Transactions on Pattern Analysis and Machine Intelligence, 2014 (accepted). [arXiv preprint].


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Please click here for our reference implementation and documentation on how to use it.


Please click here for data used in our evaluation.