Bayesian 3D shape from silhouettes

  title={Bayesian 3D shape from silhouettes},
  author={Donghoon Kim and Jonathan Ruttle and Rozenn Dahyot},
  journal={Digital Signal Processing},
This paper introduces a smooth posterior density function for inferring shapes from silhouettes. Both the likelihood and the prior are modelled using Kernel Density functions and optimisation is performed using gradient ascent algorithms. Adding a prior allows for the recovery of concave areas of the shape that are usually lost when estimating the visual hull. This framework is also extended to use colour information when it is available in addition to the silhouettes. In these cases, the… CONTINUE READING