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 new mid-level scene descriptor, comprised of explicit local shape distributions that are inferred separately at every image patch across multiple scales. Our 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 rich shape information without over-committing to any particular image explanation. These local shape distributions enable efficient and robust reconstruction of scene lighting and object-scale shape, by naturally encoding the fact that some smooth diffuse regions are more informative than others. The experimental results show the proposed shape reconstruction algorithm compares well against the state-of-art counterparts on both synthetic and real image data sets.

本文提出了一种基于扩展局部形状二次表示和局部形状分布的区分度量方法，可以用于表征物体表面形状和表面照明，实验表明该方法能够实现高效和稳健的物体表面重建，同时其对于光照和噪声的鲁棒性较强。

从阴影到局部形状