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Analysis of institutional authors

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August 15, 2022
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Monocular depth map estimation based on a multi-scale deep architecture and curvilinear saliency feature boosting

Publicated to:NEURAL COMPUTING & APPLICATIONS. 34 (19): 16423-16440 - 2022-10-01 34(19), DOI: 10.1007/s00521-022-07663-x

Authors: Abdulwahab, Saddam; Rashwan, Hatem A; Garcia, Miguel Angel; Masoumian, Armin; Puig, Domenec

Affiliations

Univ Autnoma Madrid, Dept Elect & Commun Technol, Ciudad Univ Cantoblanco, Madrid 28049, Spain - Author
Univ Rovira & Virgil, Dept Comp Engn & Math, Carretera Valls, Tarragona 43007, Spain - Author
Universidad Autónoma de Madrid - Author
Universitat Rovira i Virgili - Author

Abstract

Estimating depth from a monocular camera is a must for many applications, including scene understanding and reconstruction, robot vision, and self-driving cars. However, generating depth maps from single RGB images is still a challenge as object shapes are to be inferred from intensity images strongly affected by viewpoint changes, texture content and light conditions. Therefore, most current solutions produce blurry approximations of low-resolution depth maps. We propose a novel depth map estimation technique based on an autoencoder network. This network is endowed with a multi-scale architecture and a multi-level depth estimator that preserve high-level information extracted from coarse feature maps as well as detailed local information present in fine feature maps. Curvilinear saliency, which is related to curvature estimation, is exploited as a loss function to boost the depth accuracy at object boundaries and raise the performance of the estimated high-resolution depth maps. We evaluate our model on the public NYU Depth v2 and Make3D datasets. The proposed model yields superior performance on both datasets compared to the state-of-the-art, achieving an accuracy of 86% and showing exceptional performance at the preservation of object boundaries and small 3D structures. The code of the proposed model is publicly available at https://github.com/SaddamAbdulrhman/MDACSFB.

Keywords

curvilinear saliencydeep autoencodersmulti-scale networksCurvilinear saliencyDeep autoencodersMonocular depth map estimationMulti-scale networks

Quality index

Bibliometric impact. Analysis of the contribution and dissemination channel

The work has been published in the journal NEURAL COMPUTING & APPLICATIONS due to its progression and the good impact it has achieved in recent years, according to the agency Scopus (SJR), it has become a reference in its field. In the year of publication of the work, 2022, it was in position , thus managing to position itself as a Q1 (Primer Cuartil), in the category Software.

From a relative perspective, and based on the normalized impact indicator calculated from World Citations provided by WoS (ESI, Clarivate), it yields a value for the citation normalization relative to the expected citation rate of: 1.16. This indicates that, compared to works in the same discipline and in the same year of publication, it ranks as a work cited above average. (source consulted: ESI Nov 14, 2024)

This information is reinforced by other indicators of the same type, which, although dynamic over time and dependent on the set of average global citations at the time of their calculation, consistently position the work at some point among the top 50% most cited in its field:

  • Field Citation Ratio (FCR) from Dimensions: 3.14 (source consulted: Dimensions Jul 2025)

Specifically, and according to different indexing agencies, this work has accumulated citations as of 2025-07-16, the following number of citations:

  • WoS: 6
  • Scopus: 7

Impact and social visibility

From the perspective of influence or social adoption, and based on metrics associated with mentions and interactions provided by agencies specializing in calculating the so-called "Alternative or Social Metrics," we can highlight as of 2025-07-16:

  • The use, from an academic perspective evidenced by the Altmetric agency indicator referring to aggregations made by the personal bibliographic manager Mendeley, gives us a total of: 6.
  • The use of this contribution in bookmarks, code forks, additions to favorite lists for recurrent reading, as well as general views, indicates that someone is using the publication as a basis for their current work. This may be a notable indicator of future more formal and academic citations. This claim is supported by the result of the "Capture" indicator, which yields a total of: 6 (PlumX).

With a more dissemination-oriented intent and targeting more general audiences, we can observe other more global scores such as:

  • The Total Score from Altmetric: 0.25.
  • The number of mentions on the social network X (formerly Twitter): 1 (Altmetric).

It is essential to present evidence supporting full alignment with institutional principles and guidelines on Open Science and the Conservation and Dissemination of Intellectual Heritage. A clear example of this is: