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A unifying representation for pixel-precise distance estimation

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Abstract

We propose a new representation of distance information that is independent from any specific acquisition device, based on the size of portrayed subjects. In this alternative description, each pixel of an image is associated with the size, in real life, of what it represents. Using our proposed representation, datasets acquired with different devices can be effortlessly combined to build more powerful models, and monocular distance estimation can be performed on images acquired from devices that were never used during training. To assess the advantages of the proposed representation, we used it to train a fully convolutional neural network that predicts with pixel-precision the size of different subjects depicted in the image, as a proxy for their distance. Experimental results show that our representation, allowing the combination of heterogeneous training datasets, makes it possible for the trained network to gain better results at test time.

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Notes

  1. All sizes refer to linear size and not surface size, so they can be either height or width. For the experiments of this paper we will always use width measures.

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Acknowledgements

We gratefully acknowledge the support of NVIDIA Corporation with the donation of the Titan X Pascal GPU used for this research.

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Authors and Affiliations

  1. Dipartimento di Informatica, Sistemistica e Comunicazione, Università degli Studi di Milano-Bicocca, Viale Sarca 336, Milan, 20126, Italy

    Simone Bianco, Marco Buzzelli & Raimondo Schettini

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  1. Simone Bianco
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  2. Marco Buzzelli
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  3. Raimondo Schettini
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Correspondence to Marco Buzzelli.

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Bianco, S., Buzzelli, M. & Schettini, R. A unifying representation for pixel-precise distance estimation. Multimed Tools Appl 78, 13767–13786 (2019). https://doi.org/10.1007/s11042-018-6568-2

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