@INPROCEEDINGS{CampbellECCV07,
  author       = {Neill D.F. Campbell and George Vogiatzis and Carlos Hern\'{a}ndez and Roberto Cipolla},
  title        = {Using Multiple Hypotheses to Improve Depth-Maps for Multi-View Stereo},
  booktitle    = {10th European Conference on Computer Vision},
  year         = {2008},
  series       = {LNCS},
  volume       = {5302},
  pages        = {766-779},
  abstract     = {We propose an algorithm to improve the quality of depth-maps used for Multi-View 
  Stereo (MVS). Many existing MVS techniques make use of a two stage approach which 
  estimates depth-maps from neighbouring images and then merges them to extract a final 
  surface. Often the depth-maps used for the merging stage will contain outliers due to errors 
  in the matching process. Traditional systems exploit redundancy in the image sequence (the 
  surface is seen in many views), in order to make the final surface estimate robust to these 
  outliers. In the case of sparse data sets there is often insufficient redundancy and thus 
  performance degrades as the number of images decreases. In order to improve performance 
  in these circumstances it is necessary to remove the outliers from the depth-maps. We identify 
  the two main sources of outliers in a top performing algorithm: (1) spurious matches due to 
  repeated texture and (2) matching failure due to occlusion, distortion and lack of texture. We 
  propose two contributions to tackle these failure modes. Firstly, we store multiple depth 
  hypotheses and use a spatial consistency constraint to extract the true depth. Secondly, we 
  allow the algorithm to return an unknown state when the a true depth estimate cannot be 
  found. By combining these in a discrete label MRF optimisation we are able to obtain high 
  accuracy depth-maps with low numbers of outliers. We evaluate our algorithm in a multi-view 
  stereo framework and find it to confer state-of-the-art performance with the leading techniques, 
  in particular on the standard evaluation sparse data sets.}
}