Title: Stochastic Mesh-Based Multiview Reconstruction
Authors: John Isidoro and Stan Sclaroff
Date: July 1, 2003
Abstract:
A method for reconstruction of 3D polygonal models from multiple views
is presented. The method uses sampling techniques to construct a
texture-mapped semi-regular polygonal mesh of the object in question.
Given a set of views and segmentation of the object in each view,
constructive solid geometry is used to build a visual hull from
silhouette prisms. The resulting polygonal mesh is simplified and
subdivided to produce a semi-regular mesh. Regions of model fit
inaccuracy are found by projecting the reference images onto the mesh
from different views. The resulting error images for each view are
used to compute a probability density function, and several points are
sampled from it. Along the epipolar lines corresponding to these
sampled points, photometric consistency is evaluated. The mesh
surface is then pulled towards the regions of higher photometric
consistency using free-form deformations. This sampling-based
approach produces a photometrically consistent solution in much less
time than possible with previous multi-view algorithms given arbitrary
camera placement.