Rethinking the prior model for stereo

Hiroshi Ishikawa*, Davi Geiger


研究成果: Conference contribution

18 被引用数 (Scopus)


Sometimes called the smoothing assumption, the prior model of a stereo matching algorithm is the algorithm's expectation on the surfaces in the world. Any stereo algorithm makes assumptions about the probability to see each surface that can be represented in its representation system. Although the past decade has seen much continued progress in stereo matching algorithms, the prior models used in them have not changed much in three decades: most algorithms still use a smoothing prior that minimizes some function of the difference of depths between neighboring sites, sometimes allowing for discontinuities. However, one system seems to use a very different prior model from all other systems: the human vision system. In this paper, we first report the observations we made in examining human disparity interpolation using stereo pairs with sparse identifiable features. Then we mathematically analyze the implication of using current prior models and explain why the human system seems to use a model that is not only different but in a sense diametrically opposite from all current models. Finally, we propose two candidate models that reflect the behavior of human vision. Although the two models look very different, we show that they are closely related.

ホスト出版物のタイトルComputer Vision - ECCV 2006, 9th European Conference on Computer Vision, Proceedings
出版社Springer Verlag
ISBN(印刷版)3540338365, 9783540338369
出版ステータスPublished - 2006 1月 1
イベント9th European Conference on Computer Vision, ECCV 2006 - Graz, Austria
継続期間: 2006 5月 72006 5月 13


名前Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
3953 LNCS


Conference9th European Conference on Computer Vision, ECCV 2006

ASJC Scopus subject areas

  • 理論的コンピュータサイエンス
  • コンピュータ サイエンス(全般)


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