scholarly journals Extraction of Structural and Semantic Data from 2D Floor Plans for Interactive and Immersive VR Real Estate Exploration

Technologies ◽  
2018 ◽  
Vol 6 (4) ◽  
pp. 101 ◽  
Author(s):  
Georg Gerstweiler ◽  
Lukas Furlan ◽  
Mikhail Timofeev ◽  
Hannes Kaufmann
Keyword(s):  
Interior Design ◽  
User Study ◽  
Three Dimensional ◽  
Indoor Navigation ◽  
Indoor Environments ◽  
Floor Plan ◽  
Semantic Data ◽  
Engineering Standards ◽  
Floor Plans ◽  
Definition Of

Three-dimensional reconstructions of indoor environments are useful in various augmented and virtual scenarios. Creating a realistic virtual apartment in 3D manually does not only take time, but also needs skilled people for implementation. Analyzing a floor plan is a complicated task. Due to the lack of engineering standards in creating these drawings, they can have multiple different appearances for the same building. This paper proposes multiple models and heuristics which enable fully automated 3D reconstructions out of only a 2D floor plan. Our study focuses on floor plan analysis and definition of special requirements for a 3D building model used in a Virtual Reality (VR) setup. The proposed method automatically analyzes floor plans with a pattern recognition approach, thereby extracting accurate metric information about important components of the building. An algorithm for mesh generation and extracting semantic information such as apartment separation and room type estimation is presented. A novel method for VR interaction with interior design completes the framework. The result of the presented system is intended to be used for presenting a large number of apartments to customers. It can also be used as a base for purposes such as furnishing apartments, realistic occlusions for AR (Augmented Reality) applications such as indoor navigation or analyzing purposes. Finally, a technical evaluation and an interactive user study prove the advantages of the presented system.

scholarly journals Navigation in Indoor Environments: Does the Type of Visual Learning Stimulus Matter?

2019 ◽  
Vol 8 (6) ◽  
pp. 251 ◽  
Author(s):  
Dajana Snopková ◽  
Hana Švedová ◽  
Petr Kubíček ◽  
Zdeněk Stachoň
Keyword(s):  
User Study ◽  
Visual Learning ◽  
Indoor Navigation ◽  
Spatial Representations ◽  
Indoor Environments ◽  
Floor Plan ◽  
Structured Interviews ◽  
Virtual Tour ◽  
Before And After ◽  
The Impact

This work addresses the impact of a geovisualization’s level of realism on a user’s experience in indoor navigation. The key part of the work is a user study in which participants navigated along a designated evacuation route previously learnt in a virtual tour or traditional 2D floor plan. The efficiency and effectiveness of completing the task was measured by the number of incorrect turns during navigation and completion time. The complexity of mental spatial representations that participants developed before and after navigating the route was also evaluated. The data was obtained using several qualitative and quantitative research methods (mobile eye tracking, structured interviews, sketching of cognitive maps, creation of navigation instructions, and additional questions to evaluate spatial orientation abilities). A total of 36 subjects (17 in the “floor plan” group and 19 in the “virtual tour” group) participated in the study. The results showed that the participants from both groups were able to finish the designated navigation route, but more detailed mental spatial representations were developed by the “virtual tour” group than the “floor plan” group. The participants in the virtual tour group created richer navigation instructions both before and after evacuation, mentioned more landmarks and could recall their characteristics. Visual landmark characteristics available in the virtual tour also seemed to support the correct decision-making.

Constructing Floor Plan through Smoke Using Ultra Wideband Radar

2021 ◽  
Vol 5 (4) ◽  
pp. 1-29
Author(s):  
Weiyan Chen ◽  
Fusang Zhang ◽  
Tao Gu ◽  
Kexing Zhou ◽  
Zixuan Huo ◽  
...  
Keyword(s):  
Spatial Information ◽  
Low Cost ◽  
Indoor Navigation ◽  
Location Based Services ◽  
Ultra Wideband ◽  
Indoor Environments ◽  
Floor Plan ◽  
Construction Methods ◽  
Median Error ◽  
Indoor Scenarios

Floor plan construction has been one of the key techniques in many important applications such as indoor navigation, location-based services, and emergency rescue. Existing floor plan construction methods require expensive dedicated hardware (e.g., Lidar or depth camera), and may not work in low-visibility environments (e.g., smoke, fog or dust). In this paper, we develop a low-cost Ultra Wideband (UWB)-based system (named UWBMap) that is mounted on a mobile robot platform to construct floor plan through smoke. UWBMap leverages on low-cost and off-the-shelf UWB radar, and it is able to construct an indoor map with an accuracy comparable to Lidar (i.e., the state-of-the-art). The underpinning technique is to take advantage of the mobility of radar to form virtual antennas and gather spatial information of a target. UWBMap also eliminates both robot motion noise and environmental noise to enhance weak reflection from small objects for the robust construction process. In addition, we overcome the limited view of single radar by combining multi-view from multiple radars. Extensive experiments in different indoor environments show that UWBMap achieves a map construction with a median error of 11 cm and a 90-percentile error of 26 cm, and it operates effectively in indoor scenarios with glass wall and dense smoke.

scholarly journals AUTOMATICALLY DETERMINING SCALE WITHIN UNSTRUCTURED POINT CLOUDS

2016 ◽  
Vol XLI-B3 ◽  
pp. 617-624
Author(s):  
Jayren Kadamen ◽  
George Sithole
Keyword(s):  
Three Dimensional ◽  
Point Clouds ◽  
Indoor Navigation ◽  
Indoor Environments ◽  
Terrestrial Lidar ◽  
Detection Of Objects ◽  
The Difference ◽  
Computationally Intensive ◽  
Three Dimensional Models ◽  
General Size

Three dimensional models obtained from imagery have an arbitrary scale and therefore have to be scaled. Automatically scaling these models requires the detection of objects in these models which can be computationally intensive. Real-time object detection may pose problems for applications such as indoor navigation. This investigation poses the idea that relational cues, specifically height ratios, within indoor environments may offer an easier means to obtain scales for models created using imagery. The investigation aimed to show two things, (a) that the size of objects, especially the height off ground is consistent within an environment, and (b) that based on this consistency, objects can be identified and their general size used to scale a model. To test the idea a hypothesis is first tested on a terrestrial lidar scan of an indoor environment. Later as a proof of concept the same test is applied to a model created using imagery. The most notable finding was that the detection of objects can be more readily done by studying the ratio between the dimensions of objects that have their dimensions defined by human physiology. For example the dimensions of desks and chairs are related to the height of an average person. In the test, the difference between generalised and actual dimensions of objects were assessed. A maximum difference of 3.96% (2.93<i>cm</i>) was observed from automated scaling. By analysing the ratio between the heights (distance from the floor) of the tops of objects in a room, identification was also achieved.

scholarly journals AUTOMATICALLY DETERMINING SCALE WITHIN UNSTRUCTURED POINT CLOUDS

2016 ◽  
Vol XLI-B3 ◽  
pp. 617-624
Author(s):  
Jayren Kadamen ◽  
George Sithole
Keyword(s):  
Three Dimensional ◽  
Point Clouds ◽  
Indoor Navigation ◽  
Indoor Environments ◽  
Terrestrial Lidar ◽  
Detection Of Objects ◽  
The Difference ◽  
Computationally Intensive ◽  
Three Dimensional Models ◽  
General Size

Three dimensional models obtained from imagery have an arbitrary scale and therefore have to be scaled. Automatically scaling these models requires the detection of objects in these models which can be computationally intensive. Real-time object detection may pose problems for applications such as indoor navigation. This investigation poses the idea that relational cues, specifically height ratios, within indoor environments may offer an easier means to obtain scales for models created using imagery. The investigation aimed to show two things, (a) that the size of objects, especially the height off ground is consistent within an environment, and (b) that based on this consistency, objects can be identified and their general size used to scale a model. To test the idea a hypothesis is first tested on a terrestrial lidar scan of an indoor environment. Later as a proof of concept the same test is applied to a model created using imagery. The most notable finding was that the detection of objects can be more readily done by studying the ratio between the dimensions of objects that have their dimensions defined by human physiology. For example the dimensions of desks and chairs are related to the height of an average person. In the test, the difference between generalised and actual dimensions of objects were assessed. A maximum difference of 3.96% (2.93<i>cm</i>) was observed from automated scaling. By analysing the ratio between the heights (distance from the floor) of the tops of objects in a room, identification was also achieved.

AccessVOR: A Semantically Enriched Voronoï-Based Approach for Navigation Assistance of Wheelchair Users in Indoor Environments

2019 ◽  
Vol 73 (1) ◽  
pp. 172-191 ◽  
Author(s):  
Reda Yaagoubi ◽  
Yehia Miky ◽  
Ahmed El Shouny
Keyword(s):  
Voronoi Diagram ◽  
Three Dimensional ◽  
Relevant Information ◽  
Indoor Navigation ◽  
Indoor Environments ◽  
Network Graph ◽  
3D Navigation ◽  
Geography Markup Language ◽  
Novel Approach ◽  
Accessibility Guidelines

People with physical disabilities often face many challenges due to the non-compliance of public buildings to accessibility standards. Hence, it is necessary to provide them with relevant information about the quality of access associated with the environment they plan to visit. In this paper, we propose ‘AccessVOR’ (Accessibility assessment based on VORonoï Diagram), a novel approach that aims to automatically generate an indoor navigation network and to assess its accessibility for people moving with wheelchairs based on the American with Disabilities Act Accessibility Guidelines (ADAAG). A semantically enriched spatial database is developed based on ADAAG and the Indoor Geography Markup Language (IndoorGML) standard. A Three-Dimensional (3D) navigation-graph is then generated from the various components of an indoor environment using a Voronoï Diagram. The semantics of ADAAG allow assessing the accessibility of each segment of this navigation graph. Next, a navigation cost is allocated to this graph based on the accessibility of each segment of the network graph for navigation purposes.

A framework for the automatic annotation of car aesthetics

2007 ◽  
Vol 21 (1) ◽  
pp. 73-90 ◽  
Author(s):  
CHIARA E. CATALANO ◽  
FRANCA GIANNINI ◽  
MARINA MONTI ◽  
GIULIANA UCELLI
Keyword(s):  
Computational Models ◽  
Three Dimensional ◽  
Multimedia Data ◽  
Computer Assisted ◽  
Target Market ◽  
Automatic Annotation ◽  
Digital Models ◽  
Semantic Data ◽  
Three Dimensional Models ◽  
Definition Of

The design of a new car is guided by a set of directives indicating the target market, specific engineering, and aesthetic constraints, which may also include the preservation of the company brand identity or the restyling of products already on the market. When creating a new product, designers usually evaluate other existing products to find sources of inspiration or to possibly reuse successful solutions. In the perspective of an optimized styling workflow, great benefit could be derived from the possibility of easily retrieving the related documentation and existing digital models both from internal and external repositories. In fact, the rapid growth of resources on the Web and the widespread adoption of computer-assisted design tools have made available huge amounts of data, the utilization of which could be improved by using more selective retrieval methods. In particular, the retrieval of aesthetic elements may help designers to create digital models conforming to specific styling properties more efficiently. The aim of our research is the definition of a framework that supports (semi)automatic extraction of semantic data from three-dimensional models and other multimedia data to allow car designers to reuse knowledge and design solutions within the styling department. The first objective is then to capture and structure the explicit and implicit elements contributing to the definition of car aesthetics, which can be realistically tackled through computational models and methods. The second step is the definition of a system architecture that is able to transfer such semantic evaluation through the automatic annotation of car models.

scholarly journals A Local 3D Voronoi-Based Optimization Method for Sensor Network Deployment in Complex Indoor Environments

Sensors ◽  
2021 ◽  
Vol 21 (23) ◽  
pp. 8011
Author(s):  
Ali Afghantoloee ◽  
Mir Abolfazl Mostafavi
Keyword(s):  
Sensor Network ◽  
Optimization Methods ◽  
Optimization Method ◽  
Indoor Navigation ◽  
Built Environments ◽  
Indoor Environments ◽  
Covariance Matrix Adaptation ◽  
Sensor Network Design ◽  
People Movement ◽  
Definition Of

Optimal sensor network deployment in built environments for tracking, surveillance, and monitoring of dynamic phenomena is one of the most challenging issues in sensor network design and applications (e.g., people movement). Most of the current methods for sensor network deployment and optimization are empirical and they often result in important coverage gaps in the monitored areas. To overcome these limitations, several optimization methods have been proposed in the recent years. However, most of these methods oversimplify the environment and do not consider the complexity of 3D architectural nature of the built environments specially for indoor applications (e.g., indoor navigation, evacuation, etc.). In this paper, we propose a novel local optimization algorithm based on a 3D Voronoi diagram, which allows a clear definition of the proximity relations between sensors in 3D indoor environments. This proposed structure is integrated with an IndoorGML model to efficiently manage indoor environment components and their relations as well as the sensors in the network. To evaluate the proposed method, we compared our results with the Genetic Algorithm (GA) and the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) algorithms. The results show that the proposed method achieved 98.86% coverage which is comparable to GA and CMA-ES algorithms, while also being about six times more efficient.

scholarly journals GEOMETRICAL NETWORK MODEL GENERATION USING POINT CLOUD DATA FOR INDOOR NAVIGATION

2018 ◽  
Vol IV-4 ◽  
pp. 141-146 ◽  
Author(s):  
M. Nakagawa ◽  
R. Nozaki
Keyword(s):  
Network Model ◽  
Point Cloud ◽  
Optimal Path ◽  
Three Dimensional ◽  
Point Clouds ◽  
Indoor Navigation ◽  
Model Generation ◽  
Indoor Environments ◽  
Point Cloud Data ◽  
Cloud Data

<p><strong>Abstract.</strong> Three-dimensional indoor navigation requires various functions, such as the shortest path retrieval, obstacle avoidance, and secure path retrieval, for optimal path finding using a geometrical network model. Although the geometrical network model can be prepared manually, the model should be automatically generated using images and point clouds to represent changing indoor environments. Thus, we propose a methodology for generating a geometrical network model for indoor navigation using point clouds through object classification, navigable area estimation, and navigable path estimation. Our proposed methodology was evaluated through experiments using the benchmark of the International Society for Photogrammetry and Remote Sensing for indoor modeling. In our experiments, we confirmed that our methodology can generate a geometrical network model automatically.</p>

scholarly journals Automatic Construction of Indoor 3D Navigation Graph from Crowdsourcing Trajectories

2021 ◽  
Vol 10 (3) ◽  
pp. 146
Author(s):  
Xin Fu ◽  
Hengcai Zhang ◽  
Peixiao Wang
Keyword(s):  
Large Scale ◽  
Clustering Algorithm ◽  
Low Frequency ◽  
Indoor Navigation ◽  
Floor Plan ◽  
3D Navigation ◽  
Graph Reconstruction ◽  
Study Results ◽  
Floor Plans

Lacking indoor navigation graph has become a bottleneck in indoor applications and services. This paper presents a novel automated indoor navigation graph reconstruction approach from large-scale low-frequency indoor trajectories without any other data sources. The proposed approach includes three steps: trajectory simplification, 2D floor plan extraction and 3D navigation graph construction. First, we propose a ST-Join-Clustering algorithm to identify and simplify redundant stay points embedded in the indoor trajectories. Second, an indoor trajectory bitmap construction based on a self-adaptive Gaussian filter is developed, and we then propose a new improved thinning algorithm to extract 2D indoor floor plans. Finally, we present an improved CFSFDP algorithm with time constraints to identify the 3D topological connection points between two different floors. To illustrate the applicability of the proposed approach, we conducted a real-world case study using an indoor trajectory dataset of over 4000 indoor trajectories and 5 million location points. The case study results showed that the proposed approach improves the navigation network accuracy by 1.83% and the topological accuracy by 13.7% compared to the classical kernel density estimation approach.

Application of high-resolution field-emission LVSEM, backscatter imaging, immunogold staining to definition of the spatial distributions of two human neutrophil adherence receptors

1993 ◽  
Vol 51 ◽  
pp. 36-37
Author(s):  
Robert D. Nelson ◽  
Sharon R. Hasslen ◽  
Stanley L. Erlandsen
Keyword(s):  
Cell Surface ◽  
Surface Membrane ◽  
Three Dimensional ◽  
Human Neutrophils ◽  
Spatial Distributions ◽  
Immunogold Staining ◽  
Functional Control ◽  
Neutrophil Adherence ◽  
Definition Of ◽  
Mode Of Attachment

Receptors are commonly defined in terms of number per cell, affinity for ligand, chemical structure, mode of attachment to the cell surface, and mechanism of signal transduction. We propose to show that knowledge of spatial distribution of receptors on the cell surface can provide additional clues to their function and components of functional control.L-selectin and Mac-1 denote two receptor populations on the neutrophil surface that mediate neutrophil-endothelial cell adherence interactions and provide for targeting of neutrophil recruitment to sites of inflammation. We have studied the spatial distributions of these receptors using LVSEM and backscatter imaging of isolated human neutrophils stained with mouse anti-receptor (primary) antibody and goat anti-mouse (secondary) antibody conjugated to 12 nm colloidal gold. This combination of techniques provides for three-dimensional analysis of the expression of these receptors on different surface membrane domains of the neutrophil: the ruffles and microvilli that project from the cell surface, and the cell body between these projecting structures.

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