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Depth Estimation from a Single Image Using Line Segments only

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Advances in Artificial Intelligence – IBERAMIA 2022 (IBERAMIA 2022)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13788))

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Abstract

We present a method for depth estimation from a single image using an intermediate representation in the form of line segments. Rather than regressing depth from a chromatic image in RGB format, we explore the use an image containing line segments extracted from the original chromatic image using the Line Segment Detector (LSD), arguing that this image, even when sparse in visual data, still contains information to infer a depth image. Our proposed approach has been tested on the NYU-depth dataset for indoor scenes and on simulated images created with Airsim, seeking to assess the performance of our method with synthetic images. Our experiments show promising results confirming that it is possible to estimate a depth image from a single image containing line segments only.

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Authors and Affiliations

  1. Instituto Nacional de Astrofisica, Optica y Electronica, Luis Enrique Erro No. 1, Sta. Ma. Tonantzintla, Puebla, Mexico

    José G. Nava Zavala & Jose Martinez-Carranza

Authors
  1. José G. Nava Zavala
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  2. Jose Martinez-Carranza
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Correspondence to José G. Nava Zavala .

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Editors and Affiliations

  1. Federal University of Rio de Janeiro, Rio de Janeiro, Brazil

    Ana Cristina Bicharra Garcia

  2. Fluminense Federal University, Niterói, Brazil

    Mariza Ferro

  3. University of Cartagena, Cartagena de Indias, Colombia

    Julio Cesar Rodríguez Ribón

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Zavala, J.G.N., Martinez-Carranza, J. (2022). Depth Estimation from a Single Image Using Line Segments only. In: Bicharra Garcia, A.C., Ferro, M., Rodríguez Ribón, J.C. (eds) Advances in Artificial Intelligence – IBERAMIA 2022. IBERAMIA 2022. Lecture Notes in Computer Science(), vol 13788. Springer, Cham. https://doi.org/10.1007/978-3-031-22419-5_28

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  • DOI: https://doi.org/10.1007/978-3-031-22419-5_28

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-22418-8

  • Online ISBN: 978-3-031-22419-5

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