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Inter-Image Statistics for 3D Environment Modeling

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Abstract

In this article we present a method for automatically recovering complete and dense depth maps of an indoor environment by fusing incomplete data for the 3D environment modeling problem. The geometry of indoor environments is usually extracted by acquiring a huge amount of range data and registering it. By acquiring a small set of intensity images and a very limited amount of range data, the acquisition process is considerably simplified, saving time and energy consumption. In our method, the intensity and partial range data are registered first by using an image-based registration algorithm. Then, the missing geometric structures are inferred using a statistical learning method that integrates and analyzes the statistical relationships between the visual data and the available depth on terms of small patches. Experiments on real-world data on a variety of sampling strategies demonstrate the feasibility of our method.

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Author notes

  1. Luz A. Torres-Méndez

    Present address: CINVESTAV Campus Saltillo, Carretera Saltillo-Monterrey, Km. 13.5, Coahuila, 25900, Mexico

Authors and Affiliations

  1. Centre for Intelligent Machines, McGill University, Montreal, H3A 2A7, Canada

    Luz A. Torres-Méndez & Gregory Dudek

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  1. Luz A. Torres-Méndez
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Correspondence to Luz A. Torres-Méndez.

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Torres-Méndez, L.A., Dudek, G. Inter-Image Statistics for 3D Environment Modeling. Int J Comput Vis 79, 137–158 (2008). https://doi.org/10.1007/s11263-007-0108-2

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