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Author

Parth Bhadaniya

Bio: Parth Bhadaniya is an academic researcher from Indian Institute of Technology Madras. The author has contributed to research in topics: Workflow & Mixed reality. The author has co-authored 1 publications.

Papers
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Book ChapterDOI
10 Jan 2021
TL;DR: In this paper, an interactive user interface using a game engine within a mixed reality environment is presented for fusing as-is spatial information with the AR/VR based information in Unity 3D.
Abstract: Generating as-is 3D Models is constantly explored for various construction management applications. The industry has been dependent on either manual or semi-automated workflows for the Scan-to-BIM process, which is laborious as well as time taking. Recently machine learning has opened avenues to recognize geometrical elements from point clouds but has not been much used because of the insufficient labeled dataset. This study aims to set up a semi-automated workflow to create labeled data sets which can be used to train ML algorithms for element identification purpose. The study proposes an interactive user interface using a gaming engine within a mixed reality environment. A workflow for fusing as-is spatial information with the AR/VR based information is presented in Unity 3D. A user-friendly UI is then developed and integrated with the VR environment to help the user to choose the category of the component by visualization. This results in the generation of an accurate as-is 3D Model, which does not require much computation or time. The intention is to propose a smooth workflow to generate datasets for learning-based methodologies in a streamlined Scan-to-BIM Process. However, this process requires user domain knowledge and input. The dataset can be continuously increased and improved to get automated results later.

6 citations


Cited by
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Journal ArticleDOI
TL;DR: In this article , the authors developed an integrated process framework for Computer-Vision-Based Construction Progress Monitoring (CV-CPM), which comprises: data acquisition and 3D-reconstruction, as-built modelling, and progress assessment.

19 citations

Journal ArticleDOI
TL;DR: In this article , a universal workflow to synthesize point clouds containing both geometry and color information by utilizing the IFC model or its 3D geometry model to automatically generate annotated point clouds for semantic segmentation in deep learning is described.

4 citations

Journal ArticleDOI
TL;DR: In this paper , the relationship between data quality and model quality in scan-to-BIM process was investigated using a case study on mechanical, electrical and plumbing (MEP) scenes, where two MEP scenes were scanned with different scanning settings (angular resolutions and scanning locations).

3 citations

Proceedings ArticleDOI
24 Jun 2022
TL;DR: In this paper , the authors identified and defined twelve key factors affecting data acquisition technology and eight factors affecting sensor mounting, and a questionnaire survey was designed, and responses from professionals were used to evaluate the Relative Importance Index (RII) for the individual factors for these technologies and methods.
Abstract: The accuracy of computer vision-based progress monitoring of construction projects depends on the quality of data acquired. The data acquisition can be conducted through different vision-based sensors combined with several options for sensor mounting. Several factors affect this combination and considering these factors in selecting the acquisition technology and sensor mounting combination is critical for acquiring accurate vision-based data for the project. Currently, their definition and impact of these factors on the selection of these technologies are both subjective, and there are no formal studies to evaluate the impact. Hence, in this study, we first identify and define twelve key factors affecting data acquisition technology and eight factors affecting sensor mounting. Next, a questionnaire survey was designed, and responses from professionals were used to evaluate the Relative Importance Index (RII) for the individual factors for these technologies and methods. The obtained ratings were compared to the author's initial assessment, and the cause for a few variations obtained was justified. This study provides a clear assessment of these factors and forms a basis for selection based on the factors involved with the project requirements.
Journal ArticleDOI
TL;DR: In this paper , the authors identify several progress monitoring methods and classifies them based on the technology they use to support progress monitoring, and then they are evaluated by highlighting their advantages and limitations.
Abstract: -Progress monitoring is one of the essential tasks while executing a construction project. Effective monitoring will lead to an accurate and timely analysis of the project’s progress which is required to make vital decisions for project control. On the other hand, inefficient and delayed updates regarding the project’s progress, which is estimated by comparing the as-built status with the as-planned status, will lead to time and cost overruns. Automated progress monitoring techniques are preferred over the conventional manual data entry method as the latter is time-consuming and complex, especially if the project scope is vast. Numerous tools and technologies are being used for progress monitoring of construction projects. Therefore, it is necessary to systematically classify and evaluate them based on their advantages and limitations for successful and appropriate implementation. Hence, this article identifies several progress monitoring methods and classifies them based on the technology they use to support progress monitoring. Then they are evaluated by highlighting their advantages and limitations. Several qualitative and quantitative factors affecting the selection of these technologies for implementation have also been identified. In future, a framework for objectively identifying the project-specific technology will be developed.