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  • Springer  (8)
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  • 1
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    Optimization of Multi-LED Setups for Underwater Robotic Vision Systems (2021)
    Springer
    In:  In: Pattern Recognition. ICPR International Workshops and Challenges. , ed. by Del Bimbo, A., Cucchiara, R., Sclaroff, S., Farinella, G. M., Mei, T., Bertini, M., Escalante, H. J. and Vezzani, R. Springer, Cham, pp. 390-397, 8 pp.
    Details
    Publication Date: 2021-03-08
    Description: In deep water conditions, vision systems mounted on underwater robotic platforms require artificial light sources to illuminate the scene. The particular lighting configurations significantly influence the quality of the captured underwater images and can make their analysis much harder or easier. Nowadays, classical monolithic Xenon flashes are gradually being replaced by more flexible setups of multiple powerful LEDs. However, this raises the question of how to arrange these light sources, given different types of seawater and-depending-on different flying altitudes of the capture platforms. Hence, this paper presents a rendering based coarse-to-fine approach to optimize recent multi-light setups for underwater vehicles. It uses physical underwater light transport models and target ocean and mission parameters to simulate the underwater images as would be observed by a camera system with particular lighting setups. This paper proposes to systematically vary certain design parameters such as each LED’s orientation and analyses the rendered image properties (such as illuminated image area and light uniformity) to find optimal light configurations. We report first results on a real, ongoing AUV light design process for deep sea mission conditions.
    Type: Book chapter , NonPeerReviewed
    Format: text
    DOI: 10.1007/978-3-030-68790-8_30
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    OceanRep: Book chapter
  • 2
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    Deep Sea Robotic Imaging Simulator (2021)
    Springer
    In:  In: Pattern Recognition. ICPR International Workshops and Challenges. , ed. by Del Bimbo, A., Cucchiara, R., Sclaroff, S., Farinella, G. M., Mei, T., Bertini, M., Escalante, H. J. and Vezzani, R. Springer, Cham, pp. 375-389.
    Details
    Publication Date: 2021-08-03
    Description: Nowadays underwater vision systems are being widely applied in ocean research. However, the largest portion of the ocean - the deep sea - still remains mostly unexplored. Only relatively few image sets have been taken from the deep sea due to the physical limitations caused by technical challenges and enormous costs. Deep sea images are very different from the images taken in shallow waters and this area did not get much attention from the community. The shortage of deep sea images and the corresponding ground truth data for evaluation and training is becoming a bottleneck for the development of underwater computer vision methods. Thus, this paper presents a physical model-based image simulation solution, which uses an in-air texture and depth information as inputs, to generate underwater image sequences taken by robots in deep ocean scenarios. Different from shallow water conditions, artificial illumination plays a vital role in deep sea image formation as it strongly affects the scene appearance. Our radiometric image formation model considers both attenuation and scattering effects with co-moving spotlights in the dark. By detailed analysis and evaluation of the underwater image formation model, we propose a 3D lookup table structure in combination with a novel rendering strategy to improve simulation performance. This enables us to integrate an interactive deep sea robotic vision simulation in the Unmanned Underwater Vehicles simulator. To inspire further deep sea vision research by the community, we release the source code of our deep sea image converter to the public (https://www.geomar.de/en/omv-research/robotic-imaging-simulator).
    Type: Book chapter , NonPeerReviewed
    Format: text
    DOI: 10.1007/978-3-030-68790-8_29
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    OceanRep: Book chapter
  • 3
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    Towards Cross Domain Transfer Learning for Underwater Correspondence Search (2022)
    Springer
    Details
    Publication Date: 2022-07-11
    Description: Underwater images are challenging for correspondence search algorithms, which are traditionally designed based on images captured in air and under uniform illumination. In water however, medium interactions have a much higher impact on the light propagation. Absorption and scattering cause wavelength- and distance-dependent color distortion, blurring and contrast reductions. For deeper or turbid waters, artificial illumination is required that usually moves rigidly with the camera and thus increases the appearance differences of the same seafloor spot in different images. Correspondence search, e.g. using image features, is however a core task in underwater visual navigation employed in seafloor surveys and is also required for 3D reconstruction, image retrieval and object detection. For underwater images, it has to be robust against the challenging imaging conditions to avoid decreased accuracy or even failure of computer vision algorithms. However, explicitly taking underwater nuisances into account during the feature extraction and matching process is challenging. On the other hand, learned feature extraction models achieved high performance in many in-air problems in recent years. Hence we investigate, how such a learned robust feature model, D2Net, can be applied to the underwater environment and particularly look into the issue of cross domain transfer learning as a strategy to deal with the lack of annotated underwater training data.
    Type: Book chapter , NonPeerReviewed
    Format: text
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    OceanRep: Book chapter
  • 4
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    Gemini connector: Interfacing differentiable physical models and neural networks (2022)
    Springer
    Details
    Publication Date: 2023-01-24
    Description: Spectacular advances have been made in the field of machine vision over the past decade. While this discipline is traditionally driven by geometric models, neural networks have proven to be superior in some applications and have significantly expanded the limits of what is possible. At the same time, conventional graphic models describe the relationship between images and the associated scene with textures and light in a physically realistic manner and are an important part of photogrammetry. Differential renderers combine these approaches by enabling gradient-based optimization in fixed structures of a graphics pipeline and thus adapt the learning process of neural networks. This fusion of formalized knowledge and machine learning motivates the idea of a modular differentiable renderer in which physical and statistical models can be recombined depending on the use case. We therefore present Gemini Connector: an initiative for the modular development and combination of differentiable physical models and neural networks. We examine opportunities and problems and motivate the idea with the extension of a differentiable rendering pipeline to include models of underwater optics for the analysis of deep sea images. Finally, we discuss use cases, especially within the Cross-Domain Fusion initiative.
    Type: Article , PeerReviewed , info:eu-repo/semantics/article
    Format: text
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    OceanRep: Article in a Scientific Journal - peer-reviewed
  • 5
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    An Optical Digital Twin for Underwater Photogrammetry: GEODT - A Geometrically Verified Optical Digital Twin for Development, Evaluation, Training, Testing and Tuning of Multi-Media Refractive Algorithms (2022)
    Springer
    Details
    Publication Date: 2024-02-07
    Description: Most parts of the Earth’s surface are situated in the deep ocean. To explore this visually rather adversarial environment with cameras, they have to be protected by pressure housings. These housings, in turn, need interfaces to the world, enduring extreme pressures within the water column. Commonly, a flat window or a half-sphere of glass, called flat-port or dome-port, respectively is used to implement such kind of interface. Hence, multi-media interfaces, between water, glass and air are introduced, entailing refraction effects in the images taken through them. To obtain unbiased 3D measurements and to yield a geometrically faithful reconstruction of the scene, it is mandatory to deal with the effects in a proper manner. Hence, we propose an optical digital twin of an underwater environment, which has been geometrically verified to resemble a real water lab tank that features the two most common optical interfaces. It can be used to develop, evaluate, train, test and tune refractive algorithms. Alongside this paper, we publish the model for further extension, jointly with code to dynamically generate samples from the dataset. Finally, we also publish a pre-rendered dataset ready for use at https://git.geomar.de/david-nakath/geodt.
    Type: Article , PeerReviewed , info:eu-repo/semantics/article
    Format: text
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    OceanRep: Article in a Scientific Journal - peer-reviewed
  • 6
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    Optical Imaging and Image Restoration Techniques for Deep Ocean Mapping: A Comprehensive Survey (2022)
    Springer
    Details
    Publication Date: 2024-02-07
    Description: Visual systems are receiving increasing attention in underwater applications. While the photogrammetric and computer vision literature so far has largely targeted shallow water applications, recently also deep sea mapping research has come into focus. The majority of the seafloor, and of Earth’s surface, is located in the deep ocean below 200 m depth, and is still largely uncharted. Here, on top of general image quality degradation caused by water absorption and scattering, additional artificial illumination of the survey areas is mandatory that otherwise reside in permanent darkness as no sunlight reaches so deep. This creates unintended non-uniform lighting patterns in the images and non-isotropic scattering effects close to the camera. If not compensated properly, such effects dominate seafloor mosaics and can obscure the actual seafloor structures. Moreover, cameras must be protected from the high water pressure, e.g. by housings with thick glass ports, which can lead to refractive distortions in images. Additionally, no satellite navigation is available to support localization. All these issues render deep sea visual mapping a challenging task and most of the developed methods and strategies cannot be directly transferred to the seafloor in several kilometers depth. In this survey we provide a state of the art review of deep ocean mapping, starting from existing systems and challenges, discussing shallow and deep water models and corresponding solutions. Finally, we identify open issues for future lines of research.
    Type: Article , PeerReviewed , info:eu-repo/semantics/article
    Format: text
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    OceanRep: Article in a Scientific Journal - peer-reviewed
  • 7
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    Fusion von Tauchroboter- und Satellitenmessungen über unterschiedliche Skalen, Messmodelle und spektrale Abtastungen (2022)
    Springer
    Details
    Publication Date: 2024-02-13
    Description: In küstennahen Gewässern ist es von Vorteil, satellitengestützte optische Messungen des Meeres mit visuellen und sensorischen Beobachtungen von Tauchrobotern zu fusionieren. Obwohl Satelliten nur wenige Meter tief in Gewässer hineinschauen können, ist es möglich, generelle Wassereigenschaften oder den Bodenbewuchs von Küstengewässern zu bestimmen. Visuelle und sensorische Tauchroboterbeobachtungen sind hierzu komplementär und können auch tiefere Gewässer erreichen. Das mitgeführte künstliche Licht wird jedoch stark gestreut und erfordert andere Messmodelle. Zusätzlich sind die räumlichen und spektralen Auflösungen der Beobachtungen oftmals sehr unterschiedlich. Wir analysieren hier die damit verbundenen Problematiken und skizzieren Wege, wie die Fusion der grundverschiedenen Messungen dennoch gelingen könnte.
    Type: Article , PeerReviewed
    Format: text
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    OceanRep: Article in a Scientific Journal - peer-reviewed
  • 8
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    Design, Implementation, and Evaluation of an External Pose-Tracking System for Underwater Cameras (2024)
    Springer
    Details
    Publication Date: 2024-03-25
    Description: To advance underwater computer vision and robotics from lab environments and clear water scenarios to the deep dark ocean or murky coastal waters, representative benchmarks and realistic datasets with ground truth information are required. In particular, determining the camera pose is essential for many underwater robotic or photogrammetric applications and known ground truth is mandatory to evaluate the performance of, e.g., simultaneous localization and mapping approaches in such extreme environments. This paper presents the conception, calibration, and implementation of an external reference system for determining the underwater camera pose in real time. The approach, based on an HTC Vive tracking system in air, calculates the underwater camera pose by fusing the poses of two controllers tracked above the water surface of a tank. It is shown that the mean deviation of this approach to an optical marker-based reference in air is less than 3 mm and 0.3. Finally, the usability of the system for underwater applications is demonstrated.
    Type: Article , PeerReviewed , info:eu-repo/semantics/article
    Format: text
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    OceanRep: Article in a Scientific Journal - peer-reviewed
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