Special Issue "Underwater 3D Recording & Modelling"

A special issue of Remote Sensing (ISSN 2072-4292).

Deadline for manuscript submissions: 30 June 2020.

Special Issue Editors

Dr. Dimitrios Skarlatos
Website
Guest Editor
Department of Civil Engineering and Geomatics in Cyprus University of Technology, Cyprus
Interests: Underwater, Image-Based Modelling, UAV, Mapping, Photogrammetry, Color Correction
Prof. Dr. Fabio Bruno
Website
Guest Editor
Department of Mechanical, Energy, and Management Engineering, University of Calabria, Rende, 87036 Cosenza, Italy
Interests: 3D recording; underwater technologies; virtual reality; augmented reality
Special Issues and Collections in MDPI journals
Dr. Fabio Menna
Website
Guest Editor
3DOM - 3D Optical Metrology Unit, FBK - Bruno Kessler Foundation, via Sommarive 18, 38123 Povo-Trento, Italy
Interests: photogrammetry; 3D optical metrology; underwater; simultaneous localization and mapping; visual inertial odometry; 3D Modeling; geometric calibration; accuracy; sensor fusion; navigation; orientation; mapping; change detection; monitoring; automation
Special Issues and Collections in MDPI journals
Dr. Erica Nocerino
Website
Guest Editor
1. LIS laboratory - Laboratoire d'informatique et Systèmes, I&M Team - Images & Models, Aix-Marseille Université, CNRS, ENSAM, Université De Toulon, Polytech, Luminy, Bat. A, case 925, 163 avenue de Luminy, 13288 Marseille cedex 9, France
2. Institute of Theoretical Physics, ETH Zurich, HIT G31.7, Wolfgang-Pauli-Strasse 278093 Zurich, Switzerland
Interests: Photogrammetry; underwater; calibration; image processing; laser scanning; 3D modelling
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

Underwater (UW) 3D recording and modelling represent an open challenge for scientists and engineers in various disciplines. Sensors and algorithms developed and optimized for in-land applications are not best suited for the harsh conditions of the submerged environment. However, in the last years, we have been witnessing groundbreaking technological developments, which allow to measure, digitize, and study the underwater world with unprecedented accuracy and level of details. Photogrammetry-based approaches coupled with virtual and augmented reality (VR/AR) applications are becoming more and more diffuse in interdisciplinary communities, such as archeology, biology, industry. At the same time, acoustic and lidar sensors are leading the large-scale underwater mapping competition.

Motivated by this consideration, the current Special Issue aims to collect the best research papers that will be presented in the 2nd edition of the workshop ‘UNDERWATER 3D RECORDING & MODELLING’ organized in Limassol (Cyprus) on May 2–3, 2019, as well as to attract the latest contributions from the international community.

Authors are strongly encouraged to submit their works on innovative approaches, methodologies, and applications using acoustic, LiDAR, or image sensors in underwater photogrammetry, 3D reconstruction for VR and AR applications, cultural heritage and archaeology, 3D metrology, marine biology, 3D scanning, underwater platforms (ROV, robots, etc.), 4D modelling, bathymetry, sensor integration and data fusion, reference control, and accuracy assessment of underwater surveys.

Dr. Dimitrios Skarlatos
Dr. Fabio Bruno
Dr. Fabio Menna
Dr. Erica Nocerino
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2000 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Underwater 3D modelling
  • 3D metrology
  • Cultural heritage
  • Virtual and augmented reality
  • Bathymetry
  • Sensor fusion
  • ROV
  • 3D biological monitoring

Published Papers (4 papers)

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Open AccessArticle
DepthLearn: Learning to Correct the Refraction on Point Clouds Derived from Aerial Imagery for Accurate Dense Shallow Water Bathymetry Based on SVMs-Fusion with LiDAR Point Clouds
Remote Sens. 2019, 11(19), 2225; https://doi.org/10.3390/rs11192225 - 24 Sep 2019
Cited by 3
Abstract
The determination of accurate bathymetric information is a key element for near offshore activities; hydrological studies, such as coastal engineering applications, sedimentary processes, hydrographic surveying, archaeological mapping and biological research. Through structure from motion (SfM) and multi-view-stereo (MVS) techniques, aerial imagery can provide [...] Read more.
The determination of accurate bathymetric information is a key element for near offshore activities; hydrological studies, such as coastal engineering applications, sedimentary processes, hydrographic surveying, archaeological mapping and biological research. Through structure from motion (SfM) and multi-view-stereo (MVS) techniques, aerial imagery can provide a low-cost alternative compared to bathymetric LiDAR (Light Detection and Ranging) surveys, as it offers additional important visual information and higher spatial resolution. Nevertheless, water refraction poses significant challenges on depth determination. Till now, this problem has been addressed through customized image-based refraction correction algorithms or by modifying the collinearity equation. In this article, in order to overcome the water refraction errors in a massive and accurate way, we employ machine learning tools, which are able to learn the systematic underestimation of the estimated depths. In particular, an SVR (support vector regression) model was developed, based on known depth observations from bathymetric LiDAR surveys, which is able to accurately recover bathymetry from point clouds derived from SfM-MVS procedures. Experimental results and validation were based on datasets derived from different test-sites, and demonstrated the high potential of our approach. Moreover, we exploited the fusion of LiDAR and image-based point clouds towards addressing challenges of both modalities in problematic areas. Full article
(This article belongs to the Special Issue Underwater 3D Recording & Modelling)
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Open AccessArticle
Scale Accuracy Evaluation of Image-Based 3D Reconstruction Strategies Using Laser Photogrammetry
Remote Sens. 2019, 11(18), 2093; https://doi.org/10.3390/rs11182093 - 07 Sep 2019
Cited by 1
Abstract
Rapid developments in the field of underwater photogrammetry have given scientists the ability to produce accurate 3-dimensional (3D) models which are now increasingly used in the representation and study of local areas of interest. This paper addresses the lack of systematic analysis of [...] Read more.
Rapid developments in the field of underwater photogrammetry have given scientists the ability to produce accurate 3-dimensional (3D) models which are now increasingly used in the representation and study of local areas of interest. This paper addresses the lack of systematic analysis of 3D reconstruction and navigation fusion strategies, as well as associated error evaluation of models produced at larger scales in GPS-denied environments using a monocular camera (often in deep sea scenarios). Based on our prior work on automatic scale estimation of Structure from Motion (SfM)-based 3D models using laser scalers, an automatic scale accuracy framework is presented. The confidence level for each of the scale error estimates is independently assessed through the propagation of the uncertainties associated with image features and laser spot detections using a Monte Carlo simulation. The number of iterations used in the simulation was validated through the analysis of the final estimate behavior. To facilitate the detection and uncertainty estimation of even greatly attenuated laser beams, an automatic laser spot detection method was developed, with the main novelty of estimating the uncertainties based on the recovered characteristic shapes of laser spots with radially decreasing intensities. The effects of four different reconstruction strategies resulting from the combinations of Incremental/Global SfM, and the a priori and a posteriori use of navigation data were analyzed using two distinct survey scenarios captured during the SUBSAINTES 2017 cruise (doi: 10.17600/17001000). The study demonstrates that surveys with multiple overlaps of nonsequential images result in a nearly identical solution regardless of the strategy (SfM or navigation fusion), while surveys with weakly connected sequentially acquired images are prone to produce broad-scale deformation (doming effect) when navigation is not included in the optimization. Thus the scenarios with complex survey patterns substantially benefit from using multiobjective BA navigation fusion. The errors in models, produced by the most appropriate strategy, were estimated at around 1 % in the central parts and always inferior to 5 % on the extremities. The effects of combining data from multiple surveys were also evaluated. The introduction of additional vectors in the optimization of multisurvey problems successfully accounted for offset changes present in the underwater USBL-based navigation data, and thus minimize the effect of contradicting navigation priors. Our results also illustrate the importance of collecting a multitude of evaluation data at different locations and moments during the survey. Full article
(This article belongs to the Special Issue Underwater 3D Recording & Modelling)
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Open AccessArticle
Detecting Square Markers in Underwater Environments
Remote Sens. 2019, 11(4), 459; https://doi.org/10.3390/rs11040459 - 23 Feb 2019
Cited by 7
Abstract
Augmented reality can be deployed in various application domains, such as enhancing human vision, manufacturing, medicine, military, entertainment, and archeology. One of the least explored areas is the underwater environment. The main benefit of augmented reality in these environments is that it can [...] Read more.
Augmented reality can be deployed in various application domains, such as enhancing human vision, manufacturing, medicine, military, entertainment, and archeology. One of the least explored areas is the underwater environment. The main benefit of augmented reality in these environments is that it can help divers navigate to points of interest or present interesting information about archaeological and touristic sites (e.g., ruins of buildings, shipwrecks). However, the harsh sea environment affects computer vision algorithms and complicates the detection of objects, which is essential for augmented reality. This paper presents a new algorithm for the detection of fiducial markers that is tailored to underwater environments. It also proposes a method that generates synthetic images with such markers in these environments. This new detector is compared with existing solutions using synthetic images and images taken in the real world, showing that it performs better than other detectors: it finds more markers than faster algorithms and runs faster than robust algorithms that detect the same amount of markers. Full article
(This article belongs to the Special Issue Underwater 3D Recording & Modelling)
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Associations between Benthic Cover and Habitat Complexity Metrics Obtained from 3D Reconstruction of Coral Reefs at Different Resolutions
Remote Sens. 2020, 12(6), 1011; https://doi.org/10.3390/rs12061011 - 21 Mar 2020
Abstract
Quantifying the three-dimensional (3D) habitat structure of coral reefs is an important aspect of coral reef monitoring, as habitat architecture affects the abundance and diversity of reef organisms. Here, we used photogrammetric techniques to generate 3D reconstructions of coral reefs and examined relationships [...] Read more.
Quantifying the three-dimensional (3D) habitat structure of coral reefs is an important aspect of coral reef monitoring, as habitat architecture affects the abundance and diversity of reef organisms. Here, we used photogrammetric techniques to generate 3D reconstructions of coral reefs and examined relationships between benthic cover and various habitat metrics obtained at six different resolutions of raster cells, ranging from 1 to 32 cm. For metrics of 3D structural complexity, fractal dimension, which utilizes information on 3D surface areas obtained at different resolutions, and vector ruggedness measure (VRM) obtained at 1-, 2- or 4-cm resolution correlated well with benthic cover, with a relatively large amount of variability in these metrics being explained by the proportions of corals and crustose coralline algae. Curvature measures were, on the other hand, correlated with branching and mounding coral cover when obtained at 1-cm resolution, but the amount of variability explained by benthic cover was generally very low when obtained at all other resolutions. These results show that either fractal dimension or VRM obtained at 1-, 2- or 4-cm resolution, along with curvature obtained at 1-cm resolution, can effectively capture the 3D habitat structure provided by specific benthic organisms. Full article
(This article belongs to the Special Issue Underwater 3D Recording & Modelling)
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