Reconstructing the surface of highly specular objects is a challenging task. The shapes of diffuse and rough specular objects can be captured in an uncontrolled setting using consumer equipment. In contrast, highly specular objects have previously deterred capture in uncontrolled environments and have only been reconstructed using tailor-made hardware. We propose a method to reconstruct such objects in uncontrolled environments using only commodity hardware. As input, our method expects multi-view photographs of the specular object, its silhouettes and an environment map of its surroundings.

We compare the reflected colors in the photographs with the ones in the environment to form probability distributions over the surface normals. As the effect of inter-reflections cannot be ignored for highly specular objects, we explicitly model them when forming the probability distributions. We recover the shape of the object in an iterative process where we alternate between estimating normals and updating the shape of the object to better explain these normals.

We run experiments on both synthetic and real-world data, that show our method is robust and produces accurate reconstructions with as few as 25 input photographs.


  author       = {Godard, Clément and Hedman, Peter and Li, Wenbin and Brostow, Gabriel J.},
  title        = {{Multi-view Reconstruction of Highly Specular Surfaces in Uncontrolled Environments}},
  booktitle    = {3DV},
  year         = {2015},


We thank Moos Hueting, Tara Ganepola, Corneliu Ilisescu and Aron Monszpart for their invaluable help to get the first version of this paper ready. We also thank George Drettakis and the UCL Vision Group for their helpful comments. The authors are supported by the UK EPSRC-funded Eng. Doctorate Centre in Virtual Environments, Imaging and Visualisation (EP/G037159/1), EU project CR-PLAY (no 611089) www.cr-play.eu, and EPSRC EP/K023578/1.