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Metaheuristics for the positioning of 3D objects based on image analysis of complementary 2D photographs

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Abstract

Today, advances in 3D modeling make it possible to identically reproduce objects, animals, humans and even entire scenes. The broad applications concern video games, virtual reality or augmented reality and cinema, for example. In this article, we propose a new method to build a 3D scene directly from several complementary photographs. The positions of the objects for which we already have a 3D model will be determined by triangulation, thanks to the information extracted from the photographs, such as the outline of the objects on the images. Each pixel of the images is converted into a value that gives its distance to the nearest outline. The 3D model of the objects is then projected on the converted images, and the triangulation is done using a cost function that gives the distance of each projection of the objects to their respective outlines. A projection is considered perfect when its distance to its outlines is null, which means that the cost function gives a score of zero as well. We propose to solve this optimization problem by means of two algorithms, namely Simulated Annealing (SA) and quantum particle swarm optimization (QUAPSO).

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  1. LISSI, University Paris Est Créteil, 94400, Vitry-sur-Seine, France

    Arnaud Flori, Hamouche Oulhadj & Patrick Siarry

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  1. Arnaud Flori
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  2. Hamouche Oulhadj
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  3. Patrick Siarry
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Correspondence to Patrick Siarry.

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Flori, A., Oulhadj, H. & Siarry, P. Metaheuristics for the positioning of 3D objects based on image analysis of complementary 2D photographs. Machine Vision and Applications 32, 105 (2021). https://doi.org/10.1007/s00138-021-01229-y

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  • DOI: https://doi.org/10.1007/s00138-021-01229-y

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