eprintid: 1326253 rev_number: 40 eprint_status: archive userid: 608 dir: disk0/01/32/62/53 datestamp: 2011-10-19 07:09:09 lastmod: 2021-11-15 01:55:14 status_changed: 2011-10-19 07:09:09 type: proceedings_section metadata_visibility: show item_issues_count: 0 creators_name: Campbell, NDF creators_name: Vogiatzis, G creators_name: Hernández, C creators_name: Cipolla, R title: Using multiple hypotheses to improve depth-maps for multi-view stereo ispublished: pub divisions: UCL divisions: B04 divisions: C05 divisions: F48 note: D. Forsyth, P. Torr, and A. Zisserman (Eds.): ECCV 2008, Part I, LNCS 5302, pp. 766–779, 2008. © Springer-Verlag Berlin Heidelberg 2008 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. date: 2008 publisher: Springer-Verlag Berlin Heidelberg official_url: http://dx.doi.org/10.1007/978-3-540-88682-2_58 vfaculties: VENG oa_status: green full_text_type: other primo: open primo_central: open_green verified: verified_manual elements_source: Manually entered elements_id: 344414 doi: 10.1007/978-3-540-88682-2_58 isbn_13: 9783540886815 lyricists_name: Campbell, Neill lyricists_id: NCAMP92 full_text_status: public series: Lecture Notes in Computer Science volume: 5302 place_of_pub: Germany pagerange: 766-779 event_title: 10th European Conference on Computer Vision book_title: Computer Vision – ECCV 2008 citation: Campbell, NDF; Vogiatzis, G; Hernández, C; Cipolla, R; (2008) Using multiple hypotheses to improve depth-maps for multi-view stereo. In: Computer Vision – ECCV 2008. (pp. pp. 766-779). Springer-Verlag Berlin Heidelberg: Germany. Green open access document_url: https://discovery.ucl.ac.uk/id/eprint/1326253/1/campbell_eccv08_multi_hypo_dm.pdf