Evaluation of image-based modeling and laser scanning accuracy for emerging automated performance monitoring techniques

By M. Golparvar-Fard, Jeffrey S. Bohn, Jochen Teizer, Silvio Savarese, Feniosky Peña-Mora. 2011

414 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Civil and Structural Engineering, Geology

View Paper: https://www.semanticscholar.org/paper/c8ac792471233b81579ef5398837577a73ce4881

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Aerospace Engineering

1. Shih-Chung Jessy Kang
2. Hung-Lin Chi
3. Xiangyu Wang

In Civil and Structural Engineering

1. Shih-Chung Jessy Kang
2. Billie F. Spencer Jr.
3. Hung-Lin Chi
4. Xiangyu Wang
5. Vedhus Hoskere
6. Yasutaka Narazaki
7. Hui Li
8. Yuequan Bao

In Geology

1. Shih-Chung Jessy Kang
2. Hung-Lin Chi
3. Xiangyu Wang
4. Hui Li
5. Yuequan Bao

Citations in the corpus

• A Review of Heritage Building Information Modeling (H-BIM) (2018, background): “Currently, TLS play a very important role in a wide range of purposes that can be directly extrapolated to cultural heritage [37], including the follow-up of an intervention [38], defect detection [39], or the reproduction of existing models [40,41].”
• Advances in Computer Vision-Based Civil Infrastructure Inspection and Monitoring (2019, background): “Golparvar-Fard et al. [157] conducted a detailed comparison of image-based point clouds and laser scanning for automated performance monitoring techniques including accuracy and usability for 3D reconstruction, shape modeling, and as-built visualizations, and found that although image-based…”
• Research trends and opportunities of augmented reality applications in architecture, engineering, and construction (2013, methodology): “The methods used to determine the geometric properties from captured images or point clouds are essential in displaying an AR [58,59].”