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A Comprehensive Survey of Depth Completion Approaches

Sensors (Basel, Switzerland), 2022-09, Vol.22 (18), p.6969 [Peer Reviewed Journal]

COPYRIGHT 2022 MDPI AG ;2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;2022 by the authors. 2022 ;ISSN: 1424-8220 ;EISSN: 1424-8220 ;DOI: 10.3390/s22186969 ;PMID: 36146318

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  • Title:
    A Comprehensive Survey of Depth Completion Approaches
  • Author: Khan, Muhammad Ahmed Ullah ; Nazir, Danish ; Pagani, Alain ; Mokayed, Hamam ; Liwicki, Marcus ; Stricker, Didier ; Afzal, Muhammad Zeshan
  • Subjects: Color imagery ; Deep learning ; depth completion ; depth maps ; Image filters ; image-guidance ; Lidar ; Machine Learning ; Maskininlärning ; Methods ; Neural networks ; Optical radar ; Propagation ; Remote sensing ; Review ; Semantics ; Sensors ; Sparsity ; State-of-the-art reviews ; Surveys ; Taxonomy
  • Is Part Of: Sensors (Basel, Switzerland), 2022-09, Vol.22 (18), p.6969
  • Description: Depth maps produced by LiDAR-based approaches are sparse. Even high-end LiDAR sensors produce highly sparse depth maps, which are also noisy around the object boundaries. Depth completion is the task of generating a dense depth map from a sparse depth map. While the earlier approaches focused on directly completing this sparsity from the sparse depth maps, modern techniques use RGB images as a guidance tool to resolve this problem. Whilst many others rely on affinity matrices for depth completion. Based on these approaches, we have divided the literature into two major categories; unguided methods and image-guided methods. The latter is further subdivided into multi-branch and spatial propagation networks. The multi-branch networks further have a sub-category named image-guided filtering. In this paper, for the first time ever we present a comprehensive survey of depth completion methods. We present a novel taxonomy of depth completion approaches, review in detail different state-of-the-art techniques within each category for depth completion of LiDAR data, and provide quantitative results for the approaches on KITTI and NYUv2 depth completion benchmark datasets.
  • Publisher: Basel: MDPI AG
  • Language: English
  • Identifier: ISSN: 1424-8220
    EISSN: 1424-8220
    DOI: 10.3390/s22186969
    PMID: 36146318
  • Source: Open Access: PubMed Central
    DOAJ Directory of Open Access Journals
    Geneva Foundation Free Medical Journals at publisher websites
    AUTh Library subscriptions: ProQuest Central
    SWEPUB Freely available online
    ROAD: Directory of Open Access Scholarly Resources
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