Non-rigid structure from motion (nrsfm), is a long standing and central problem in computer vision and its solution is necessary for obtaining 3D information from multiple images when the scene is dynamic. A main issue regarding the further development of this important computer vision topic, is the lack of high quality data sets. We here address this issue by presenting a data set created for this purpose, which is made publicly available, and considerably larger than the previous state of the art. To validate the applicability of this data set, and provide an investigation into the state of the art of nrsfm, including potential directions forward, we here present a benchmark and a scrupulous evaluation using this data set. This benchmark evaluates 18 different methods with available code that reasonably spans the state of the art in sparse nrsfm. This new public data set and evaluation protocol will provide benchmark tools for further development in this challenging field.

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We propose a method for direct comparison of rendered images with a corresponding photograph in order to analyze the optical properties of physical objects and test the appropriateness of appearance models. To this end, we provide a practical method for aligning a known object and a point-like light source with the configuration observed in a photograph. Our method is based on projective transformation of object edges and silhouette matching in the image plane. To improve the similarity between rendered and photographed objects, we introduce models for spatially varying roughness and a model where the distribution of light transmitted by a rough surface influences direction-dependent subsurface scattering. Our goal is to support development toward progressive refinement of appearance models through quantitative validation.