scholarly journals A Survey on Data-driven Dictionary-based Methods for 3D Modeling

2018 ◽  
Vol 37 (2) ◽  
pp. 577-601 ◽  
Author(s):  
Thibault Lescoat ◽  
Maks Ovsjanikov ◽  
Pooran Memari ◽  
Jean-Marc Thiery ◽  
Tamy Boubekeur
Keyword(s):  
3D Modeling ◽  
Data Driven

Creating a longitudinal, data-driven 3D model of change over time in a postindustrial landscape using GIS and CityEngine

2018 ◽  
Vol 8 (4) ◽  
pp. 434-447 ◽  
Author(s):  
John David McEwen Arnold ◽  
Don Lafreniere
Keyword(s):  
Big Data ◽  
Longitudinal Data ◽  
3D Modeling ◽  
Heritage Management ◽  
Data Driven ◽  
Change Over Time ◽  
Content Type ◽  
Modeling Software ◽  
Copper Country ◽  
Over Time

Purpose The purpose of this paper is to create a longitudinal data-driven model of change over time in a postindustrial landscape, using the “Copper Country” of Michigan’s Upper Peninsula as a case study. The models resulting from this project will support the heritage management and public education goals of the contemporary communities and Keweenaw National Historical Park that administer this nationally significant mining region through accessible, engaging, and interpretable digital heritage. Design/methodology/approach The paper applies Esri’s CityEngine procedural modeling software to an existing historical big data set. The Copper Country Historical Spatial Data Infrastructure, previously created by the HESA lab, contains over 120,000 spatiotemporally specific building footprints and other built environment variables. This project constructed a pair of 3D digital landscapes comparing the built environments of 1917 and 1949, reflecting the formal and functional evolution of several of the most important copper mining, milling, and smelting districts of Michigan’s Keweenaw Peninsula. Findings This research discovered that CityEngine, while intended for rapid 3D modeling of the contemporary urban landscape, was sufficiently robust and flexible to be applied to modeling serial historic industrial landscapes. While this novel application required some additional coding and finish work, by harnessing this software to existing big data sets, 48,000 individual buildings were rapidly visualized using several key variables. Originality/value This paper presents a new and useful application of an existing 3D modeling software, helping to further illuminate and inform the management and conservation of the rich heritage of this still-evolving postindustrial landscape.

scholarly journals Automatic Extraction of Indoor Spatial Information from Floor Plan Image: A Patch-Based Deep Learning Methodology Application on Large-Scale Complex Buildings

2021 ◽  
Vol 10 (12) ◽  
pp. 828
Author(s):  
Hyunjung Kim ◽  
Seongyong Kim ◽  
Kiyun Yu
Keyword(s):  
3D Modeling ◽  
Detection Rate ◽  
Large Scale ◽  
Spatial Information ◽  
Recognition Accuracy ◽  
Indoor Navigation ◽  
Data Driven ◽  
Automatic Extraction ◽  
Floor Plan ◽  
Plan Analysis

Automatic floor plan analysis has gained increased attention in recent research. However, numerous studies related to this area are mainly experiments conducted with a simplified floor plan dataset with low resolution and a small housing scale due to the suitability for a data-driven model. For practical use, it is necessary to focus more on large-scale complex buildings to utilize indoor structures, such as reconstructing multi-use buildings for indoor navigation. This study aimed to build a framework using CNN (Convolution Neural Networks) for analyzing a floor plan with various scales of complex buildings. By dividing a floor plan into a set of normalized patches, the framework enables the proposed CNN model to process varied scale or high-resolution inputs, which is a barrier for existing methods. The model detected building objects per patch and assembled them into one result by multiplying the corresponding translation matrix. Finally, the detected building objects were vectorized, considering their compatibility in 3D modeling. As a result, our framework exhibited similar performance in detection rate (87.77%) and recognition accuracy (85.53%) to that of existing studies, despite the complexity of the data used. Through our study, the practical aspects of automatic floor plan analysis can be expanded.

Data-driven suggestions for creativity support in 3D modeling

2010 ◽  
Vol 29 (6) ◽  
pp. 1-10 ◽  
Author(s):  
Siddhartha Chaudhuri ◽  
Vladlen Koltun
Keyword(s):  
3D Modeling ◽  
Data Driven ◽  
Creativity Support

scholarly journals Data-Driven Analytics towards Software Sustainability: The Case of Open-Source Multimedia Tools on Cultural Storytelling

2021 ◽  
Vol 13 (3) ◽  
pp. 1079
Author(s):  
Michail D. Papamichail ◽  
Andreas L. Symeonidis
Keyword(s):  
Cultural Heritage ◽  
Open Source ◽  
3D Modeling ◽  
Necessary Conditions ◽  
Future Generations ◽  
Data Driven ◽  
Multimedia Tools ◽  
Software Artifact ◽  
Historical Sites ◽  
Online Software

The continuous evolution of modern software technologies combined with the deluge of available “ready-to-use” data has triggered revolutionary breakthroughs in several domains, preservation of cultural heritage included. This breakthrough is more than obvious just by considering the numerous multimedia tools and frameworks that actually serve as a means of providing enhanced cultural storytelling experiences (e.g., navigation in historical sites using VR, 3D modeling of artifacts, or even holograms), which are now readily available. In this context and inspired by the vital importance of sustainability as a concept that expresses the need to create the necessary conditions for future generations to use and evolve present artifacts, we target the software engineering domain and propose a systematic way towards measuring the extent to which a software artifact developed and applied in the cultural heritage domain is sustainable. To that end, we present a data-driven methodology that harnesses data residing in online software repositories and involves the analysis of various open-source multimedia tools and frameworks.

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