  Highly accurate 3D volumetric reconstruction is still an open research topic
where the main difficulty is usually related to merging some rough estimations
with high frequency details. One of the most promising methods is the fusion
between multi-view stereo and photometric stereo images. Beside the intrinsic
difficulties that multi-view stereo and photometric stereo in order to work
reliably, supplementary problems arise when considered together.
  In this work, we present a volumetric approach to the multi-view photometric
stereo problem. The key point of our method is the signed distance field
parameterisation and its relation to the surface normal. This is exploited in
order to obtain a linear partial differential equation which is solved in a
variational framework, that combines multiple images from multiple points of
view in a single system. In addition, the volumetric approach is naturally
implemented on an octree, which allows for fast ray-tracing that reliably
alleviates occlusions and cast shadows.
  Our approach is evaluated on synthetic and real data-sets and achieves
state-of-the-art results.
