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A Comprehensive Survey of Depth Completion Approaches
Department of Computer Science, Technical University of Kaiserslautern, 67663 Kaiserslautern, Germany; Mindgarage, Technical University of Kaiserslautern, 67663 Kaiserslautern, Germany; German Research Institute for Artificial Intelligence (DFKI), 67663 Kaiserslautern, Germany.ORCID iD: 0000-0001-8949-4243
Department of Computer Science, Technical University of Kaiserslautern, 67663 Kaiserslautern, Germany; Mindgarage, Technical University of Kaiserslautern, 67663 Kaiserslautern, Germany; German Research Institute for Artificial Intelligence (DFKI), 67663 Kaiserslautern, Germany.ORCID iD: 0000-0001-6364-8427
German Research Institute for Artificial Intelligence (DFKI), 67663 Kaiserslautern, Germany.
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.ORCID iD: 0000-0001-6158-3543
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2022 (English)In: Sensors, E-ISSN 1424-8220, Vol. 22, no 18, article id 6969Article, review/survey (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
MDPI, 2022. Vol. 22, no 18, article id 6969
Keywords [en]
depth completion, depth maps, image-guidance
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering Computer Sciences
Research subject
Machine Learning
Identifiers
URN: urn:nbn:se:ltu:diva-93285DOI: 10.3390/s22186969ISI: 000857032000001PubMedID: 36146318Scopus ID: 2-s2.0-85138350056OAI: oai:DiVA.org:ltu-93285DiVA, id: diva2:1699564
Note

Validerad;2022;Nivå 2;2022-09-28 (joosat);

Available from: 2022-09-28 Created: 2022-09-28 Last updated: 2024-04-08Bibliographically approved

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Mokayed, HamamLiwicki, Marcus

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