GEODIM: A Semantic Model-Based System for 3D Recognition of Industrial Scenes

Perez-Gallardo, Yuliana; López-Cuadrado, José Luis; García-Crespo, Ángel; Jesús, Cynthya García de

doi:10.1007/978-3-319-51905-0_7

Cited by 18 publications

(7 citation statements)

References 31 publications

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“…The semantic web is one of the predominant approaches to knowledge representation in different domains and one of the main trends in the evolution of the web [4]. It gains increasing attention in the context of graphical systems and XR, e.g., for photogrammetry [3], molecular visualization [46,47], content description and retrieval [43,44], design of industrial spaces [38], archaeology [13] as well as feature-based data exchange between heterogeneous CAD systems [53,60]. Due to the use of the semantic web in our method, XR behavior can be represented with general or domain knowledge, thus being intelligible to average users and domain experts who are not IT-specialists.…”

Section: Methods Of Tracking and Registering Behavior Of Xr Environmementioning

confidence: 99%

Creating explorable extended reality environments with semantic annotations

Flotyński

2020

Multimed Tools Appl

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The main element of extended reality (XR) environments is behavior-rich 3D content consisting of objects that act and interact with one another as well as with users. Such actions and interactions constitute the evolution of the content over time. Multiple application domains of XR, e.g., education, training, marketing, merchandising, and design, could benefit from the analysis of 3D content changes based on general or domain knowledge comprehensible to average users or domain experts. Such analysis can be intended, in particular, to monitor, comprehend, examine, and control XR environments as well as users’ skills, experience, interests and preferences, and XR objects’ features. However, it is difficult to achieve as long as XR environments are developed with methods and tools that focus on programming and 3D modeling rather than expressing domain knowledge accompanying content users and objects, and their behavior. The main contribution of this paper is an approach to creating explorable knowledge-based XR environments with semantic annotations. The approach combines description logics with aspect-oriented programming, which enables knowledge representation in an arbitrary domain as well as transformation of available environments with minimal users’ effort. We have implemented the approach using well-established development tools and exemplify it with an explorable immersive car showroom. The approach enables efficient creation of explorable XR environments and knowledge acquisition from XR.

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Section: Methods Of Tracking and Registering Behavior Of Xr Environmementioning

confidence: 99%

Creating explorable extended reality environments with semantic annotations

Flotyński

2020

Multimed Tools Appl

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“…Low (3D graphics) High (application domain) De Troyer et al [5]- [9] general Gutiérrez et al [10], [11] humanoids Kalogerakis et al [12] -Spagnuolo et al [13]- [15] humanoids Floriani et al [16], [17] -Kapahnke et al [18] general Albrecht et al [19] interior design Latoschik et al [20]- [22] general Drap et al [23] archaeology Trellet et al [24], [25] molecules Perez-Gallardo et al [26] -…”

Section: Level Of Abstractionmentioning

confidence: 99%

Semantic Representation of Domain Knowledge for Professional VR Training

et al. 2021

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Domain-specific knowledge representation is an essential element of efficient management of professional training. Formal and powerful knowledge representation for training systems can be built upon the semantic web standards, which enable reasoning and complex queries against the content. Virtual reality training is currently used in multiple domains, in particular, if the activities are potentially dangerous for the trainees or require advanced skills or expensive equipment. However, the available methods and tools for creating VR training systems do not use knowledge representation. Therefore, creation, modification and management of training scenarios is problematic for domain experts without expertise in programming and computer graphics. In this paper, we propose an approach to creating semantic virtual training scenarios, in which users’ activities, mistakes as well as equipment and its possible errors are represented using domain knowledge understandable to domain experts. We have verified the approach by developing a user-friendly editor of VR training scenarios for electrical operators of high-voltage installations.

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“…For this reason, prior knowledge cannot rely on P&IDs. [17] used topological information to extract semantic labels for four object classes: pipes, planes, elbows and valves. They detect cylinders with 86% precision and 92% recall.…”

Section: State-of-researchmentioning

confidence: 99%

State-of-Practice on As-Is Modelling of Industrial Facilities

Agapaki

Brilakis

2018

Advanced Computing Strategies for Engineering

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90% of the time needed for the conversion from point clouds to 3D models of industrial facilities is spent on geometric modelling due to the sheer number of Industrial Objects (IOs) of each plant. Hence, cost reduction is only possible by automating modelling. Our previous work has successfully identified the most frequent industrial objects which are in descending order: electrical conduit, straight pipes, circular hollow sections, elbows, channels, solid bars, I-beams, angles, flanges and valves. We modelled those on a state-of-theart software, EdgeWise and then evaluated the performance of this software for pipeline and structural modelling. The modelling of pipelines is summarized in three basic steps: (a) automated extraction of cylinders, (b) their semantic classification and (c) manual extraction and editing of pipes. The results showed that cylinders are modelled with 75 % recall and 62 % precision on average. We discovered that pipes, electrical conduit and circular hollow sections require 80 % of the Total Modelling Hours (TMH) of the 10 most frequent IOs to build the plant model. TMH was then compared to modelling hours in Revit and showed that 67 % of pipe modelling time is saved by EdgeWise. This paper is the first to evaluate state-of-the-art industrial modelling software. These findings help in better understanding the problem and serve as the foundation for researchers who are interested in solving it.

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GEODIM: A Semantic Model-Based System for 3D Recognition of Industrial Scenes

Cited by 18 publications

References 31 publications

Creating explorable extended reality environments with semantic annotations

Creating explorable extended reality environments with semantic annotations

Semantic Representation of Domain Knowledge for Professional VR Training

State-of-Practice on As-Is Modelling of Industrial Facilities

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