Neural rendering-based semantic point cloud retrieval for indoor construction progress monitoring
Automation in Construction, ISSN: 0926-5805, Vol: 164, Page: 105448
2024
- 2Citations
- 25Captures
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Article Description
Computer vision has been exploited to retrieve semantic and geometric information for indoor construction progress monitoring. However, existing methods lack the capability to retrieve well coupled semantic and geometric information, which leads to a loss of accuracy and limits the applicability. This study introduces a novel approach called Semantic Reconstruction enabled by Neural Radiance Field (SRecon-NeRF) that extracts highly coupled semantic and geometric information as semantic point cloud. Moreover, a progress estimation strategy is designed to execute progress estimation logic. The evaluation results demonstrate that SRecon-NeRF outperforms the existing semantic-based methods by 24% in accuracy and 75% in speed. It achieves a 36% enhancement in accuracy and an 83.3% boost in speed compared to the geometric-based methods. The utilization of SRecon-NeRF as an information retrieval method in real-world scenarios can improve the practical accuracy, speed, and applicability of CV-based ICPM. Consequently, this can facilitate the widespread digital transformation of ICPM.
Bibliographic Details
http://www.sciencedirect.com/science/article/pii/S0926580524001845; http://dx.doi.org/10.1016/j.autcon.2024.105448; http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85192256248&origin=inward; https://linkinghub.elsevier.com/retrieve/pii/S0926580524001845; https://dx.doi.org/10.1016/j.autcon.2024.105448
Elsevier BV
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