3D Scanning: A Comprehensive Survey

Daneshmand, Morteza; Helmi, Ahmed; Avots, Egils; Noroozi, Fatemeh; Alisinanoglu, Fatih; Arslan, Hasan Sait; Gorbova, Jelena; Haamer, Rain Eric; Ozcinar, Cagri; Anbarjafari, Gholamreza

doi:10.48550/arxiv.1801.08863

Cited by 11 publications

(11 citation statements)

References 131 publications

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“…Today methods of surface geometry reconstruction (3D scanning) are very versatile and common in many applications. A good survey of such methods is provided by Daneshmand et al in [4]. For the PSP/TSP methods it is preferred that the mapping is available with information acquired directly from the luminescence image.…”

Section: General Description Of the Problemmentioning

confidence: 99%

Correction of self-illumination for pressure and temperature sensitive paints

Gobyzov

Rabetskiy²,

Bilskiy³

2022

EPJ Web Conf.

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The present work reports self-illumination correction procedure and results of its experimental testing on a simple model. In the procedure reflectivity value is assessed directly from the set of model images captured at wind-off conditions with different reflecting surfaces covered or removed. The paper covers the theoretical part of the problem and describes exact implementation of a processing sequence, which included image mapping on a 3-D surface, building a mesh of surface elements, evaluation of radiative exchange between elements and calculation of the corrected brightness map.

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Section: General Description Of the Problemmentioning

confidence: 99%

Correction of self-illumination for pressure and temperature sensitive paints

Gobyzov

Rabetskiy²,

Bilskiy³

2022

EPJ Web Conf.

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“…More specifically, in water surveillance tasks, two equally important issues arise: namely, the inference of three-dimensional information pertaining to the topology of a ground surface and the detection of land and water areas. In general, three-dimensional information can be extracted by incorporating either LiDAR or RGB-D sensors in an UAV's sensor suite [13], while relatively to water monitoring, photogrammetry has been extensively used to obtain 3D information [14,15]. When it comes to detecting water surfaces from top-view images of the Earth's surface, the most widely used approaches employ multi-spectral cameras to obtain multiple images of the same area and subsequently apply a thresholding process, which, if tuned correctly can produce a valid classifier of water and land pixels (see, e.g., [16]).…”

Section: Related Literaturementioning

confidence: 99%

A Vision-Based Motion Control Framework for Water Quality Monitoring Using an Unmanned Aerial Vehicle

et al. 2022

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In this paper, we present a vision-aided motion planning and control framework for the efficient monitoring and surveillance of water surfaces using an Unmanned Aerial Vehicle (UAV). The ultimate goal of the proposed strategy is to equip the UAV with the necessary autonomy and decision-making capabilities to support First Responders during emergency water contamination incidents. Toward this direction, we propose an end-to-end solution, based on which the First Responder indicates visiting and landing waypoints, while the envisioned strategy is responsible for the safe and autonomous navigation of the UAV, the refinement of the way-point locations that maximize the visible water surface area from the onboard camera, as well as the on-site refinement of the appropriate landing region in harsh environments. More specifically, we develop an efficient waypoint-tracking motion-planning scheme with guaranteed collision avoidance, a local autonomous exploration algorithm for refining the way-point location with respect to the areas visible to the drone’s camera, water, a vision-based algorithm for the on-site area selection for feasible landing and finally, a model predictive motion controller for the landing procedure. The efficacy of the proposed framework is demonstrated via a set of simulated and experimental scenarios using an octorotor UAV.

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“…Pros: Point clouds can represent arbitrarily complex geometry with a finite number of points, though fidelity may vary. Point clouds are often the native output of 3D scanning software, making them relatively easy to create [28]. Cons: Point Clouds do not directly describe topology of an object [29].…”

Section: Point Cloudsmentioning

confidence: 99%

Deep Generative Models in Engineering Design: A Review

Regenwetter¹,

Nobari²,

Ahmed³

2021

Preprint

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Automated design synthesis has the potential to revolutionize the modern human design process and improve access to highly optimized and customized products across countless industries. Successfully adapting generative Machine Learning to design engineering may be the key to such automated design synthesis and is a research subject of great importance. We present a review and analysis of Deep Generative Learning models in engineering design. Deep Generative Models (DGMs) typically leverage deep networks to learn from an input dataset and learn to synthesize new designs. Recently, DGMs such as Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), feedforward Neural Networks (NNs) and certain Deep Reinforcement Learning (DRL) frameworks have shown promising results in design applications like structural optimization, materials design, and shape synthesis. The prevalence of DGMs in Engineering Design has skyrocketed since 2016. Anticipating continued growth, we conduct a review of recent advances with the hope of benefitting researchers interested in DGMs for design. We structure our review as an exposition of the algorithms, datasets, representation methods, and applications commonly used in the current literature. In particular, we discuss key works that have introduced new techniques and methods in DGMs, successfully applied DGMs to a design-related domain, or directly supported development of DGMs through datasets or auxiliary methods. We further identify key challenges and limitations currently seen in DGMs across design fields, such as design creativity, handling complex constraints and objectives, and modeling both form and functional performance simultaneously. In our discussion we identify possible solution pathways as key areas on which to target future work.

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3D Scanning: A Comprehensive Survey

Cited by 11 publications

References 131 publications

Correction of self-illumination for pressure and temperature sensitive paints

Correction of self-illumination for pressure and temperature sensitive paints

A Vision-Based Motion Control Framework for Water Quality Monitoring Using an Unmanned Aerial Vehicle

Deep Generative Models in Engineering Design: A Review

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