Exploring Past and Present: VR Reconstruction of the Berlin Gendarmenmarkt

Derdaele, Jens; Shekhawat, Yash; Vergauwen, Maarten

doi:10.1109/ismar-adjunct.2018.00088

Cited by 3 publications

(3 citation statements)

References 16 publications

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“…2) 3D Reconstruction: Automatic generation of 3D models using photogrammetry is handled by the 3D reconstruction (3DR) module [3]. Video and image files, as well as output from STOL, STBL, the shot detection and texture proposal modules provide important input to the 3DR task.…”

Section: B Content Extraction From Visual Data 1) Localisation and Te...mentioning

confidence: 99%

V4Design: Intelligent Analysis and Integration of Multimedia Content for Creative Industries

Symeonidis

Meditskos

Vrochidis

et al. 2023

IEEE Systems Journal

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Nowadays vast amounts of multimedia content is being produced, archived and digitised, resulting in great troves of data of interest. Examples include user-generated content, such as images, videos, text and audio posted by users on social media and wikis, or content provided through official publishers and distributors, such as digital libraries, organisations and online museums. This digital content can serve as a valuable source of inspiration to the creative industries, such as architecture and gaming, to produce new innovative assets or to enhance and (re-)use existing ones. However, in its current form, this content is difficult to be reused and repurposed due to the lack of appropriate solutions for its retrieval, analysis and integration into the design process. In this paper we present V4Design, a novel framework for the automatic content analysis, linking and seamless transformation of heterogeneous multimedia content to help architects and virtual reality game designers establish innovative value chains and end-user applications. By integrating and intelligently combining state-of-the-art technologies in computer vision, 3D generation, text analysis, generation and semantic integration and interlinking, V4Design provides architects and video game designers with innovative tools to draw inspiration from archive footage and documentaries, inspiring and eventually supporting the design process.

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Section: B Content Extraction From Visual Data 1) Localisation and Te...mentioning

confidence: 99%

V4Design: Intelligent Analysis and Integration of Multimedia Content for Creative Industries

Symeonidis

Meditskos

Vrochidis

et al. 2023

IEEE Systems Journal

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“…Such technologies capture geometric information at the few cm-level, thus allowing for accurate geometric representation of real scenes in VR. Point cloud technologies have been used to create virtual environments in several disciplines such as in gaming and filmmaking, preservation of cultural heritage, and geoscience for field trips [43][44][45][46][47][48]. Another essential aspect of virtual environments is textures, as they give a sense of realism.…”

Section: The Ready Room and Virtual Environmentmentioning

confidence: 99%

Surveying Reality (SurReal): Software to Simulate Surveying in Virtual Reality

Bolkas

Chiampi

Fioti

et al. 2021

IJGI

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Experiential learning through outdoor labs is an integral component of surveying education. Cancelled labs as a result of weather, the inability to visit a wide variety of terrain location, recent distance education requirements create significant instructional challenges. In this paper, we present a software solution called surveying reality (SurReal); this software allows for students to conduct immersive and interactive virtual reality labs. This paper discusses the development of a virtual differential leveling lab. The developed software faithfully replicates major steps followed in the field and any skills learned in virtual reality are transferable in the physical world. Furthermore, this paper presents a novel technique for leveling multi-legged objects like a tripod on variable terrain. This method relies solely on geometric modeling and does not require physical simulation of the tripod. This increases efficiency and ensures that the user experiences at least 60 frames per second, thus reducing lagging and creating a pleasant experience for the user. We conduct two leveling examples, a three-benchmark loop and a point-to-point leveling line. Both surveys had a misclosure of 1 mm due to observational random errors, which demonstrated that leveling can be conducted with mm-level precision, much like in the physical world.

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“…This allows users to generate 3D models of their buildings of interest from a set of retrieved and analyzed images starting from keyword based queries. The current imple-mentation of the process builds upon Reality Capture 32 which was found to be a reliable reconstruction software in previous work[73]. Once new data is available from the STBOL algorithm, a set of suitable frames are passed to the reconstruction module.…”

mentioning

confidence: 99%

Processing existing building geometry for reuse as Linked Data

Bassier

Bonduel

Derdaele

et al. 2020

Automation in Construction

Self Cite

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The Web currently hosts a vast amount of 2D images and 3D building models.Each repository has its own data structure and a limited set of semantics according to their own needs. With the advent of Semantic Web Technologies, the opportunity arises to combine these heterogeneous data sets and publish them as Linked Data. It is within the scope of this research to investigate whether online 2D and 3D content can be enriched, published and reused as RDF. The emphasis of this work is on extracting building component information from online building geometry and publishing it as Linked Data. An interpretation framework is presented that takes as input any building mesh and computes its building components through machine learning techniques. Additionally, a Structure-from-Motion pipeline is proposed that provides similar outputs and links the 2D imagery to the reconstructed 3D building geometry. The experiments show that, even though the building content originates from different sources and was not modeled according to any standards, building geometry in online repositories and photogrammetric reconstructions can be semantically enriched with component information using terminology from Linked Building Data ontologies such as BOT, PRODUCT and OMG/FOG/GOM. This is an important step towards making structureless geometric information retrievable, linkable and thus reusable over the Web.

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Exploring Past and Present: VR Reconstruction of the Berlin Gendarmenmarkt

Cited by 3 publications

References 16 publications

V4Design: Intelligent Analysis and Integration of Multimedia Content for Creative Industries

V4Design: Intelligent Analysis and Integration of Multimedia Content for Creative Industries

Surveying Reality (SurReal): Software to Simulate Surveying in Virtual Reality

Processing existing building geometry for reuse as Linked Data

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