@article{
 title = {Fast and accurate multi-view reconstruction by multi-stage prioritised matching},
 type = {article},
 year = {2015},
 identifiers = {[object Object]},
 pages = {576-587},
 volume = {9},
 id = {e08e245c-4650-3923-9b9f-dbd8a6e81068},
 created = {2019-09-15T16:34:27.796Z},
 file_attached = {false},
 profile_id = {bddcf02d-403b-3b06-9def-6d15cc293e20},
 last_modified = {2019-09-15T17:27:45.546Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 source_type = {JOUR},
 private_publication = {false},
 abstract = {In this paper, we propose a multi-view stereo re-construction method which creates a three-dimensional point cloud of a scene from multiple calibrated im-ages captured from different viewpoints. The method is based on a prioritized match expansion technique, which starts from a sparse set of seed points, and it-eratively expands them into neighboring areas by us-ing multiple expansion stages. Each seed point rep-resents a surface patch and has a position and a sur-face normal vector. The location and surface normal of the seeds are optimized using a homography-based lo-cal image alignment. The propagation of seeds is per-formed in a prioritized order in which the most promis-ing seeds are expanded first and removed from the list of seeds. The first expansion stage proceeds until the list of seeds is empty. In the following expansion stages, the current reconstruction may be further expanded by finding new seeds near the boundaries of the current reconstruction. The prioritized expansion strategy al-lows efficient generation of accurate point clouds and our experiments show its benefits compared with non-prioritized expansion. In addition, a comparison to the widely used patch-based multi-view stereo software (PMVS) shows that our method is significantly faster and produces more accurate and complete reconstruc-tions.},
 bibtype = {article},
 author = {Ylimäki, Markus and Kannala, Juho and Holappa, Jukka and Brandt, Sami S and Heikkilä, Janne},
 journal = {IET Computer Vision},
 number = {4}
}

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