ISPRS Commission V Symposium: Image Engineering And Vision Metrology

Mills, J. P.; Chandler, Jim H.

doi:10.1111/j.1477-9730.2007.00418.x

Cited by 6 publications

(2 citation statements)

References 2 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…When historical structures' surfaces with relatively flat geometries are investigated, the integration in 3D space is, according to the bibliography, achieved through measuring the 3D positioning of control points (usually the start and end-point) of the scan lines [297][298][299][300]. Apart from registration, the availability of a dense geometric 3D model or point cloud can also assist the spatial correction of GRP data collected for structures with more complex geometries, the most common example being historic bridges [301][302][303][304][305][306][307][308][309]. Penetrating radar exploration of historic structure's columns may require only a simplified knowledge of the geometrical shapes [310,311].…”

Section: Radar Ultrasonic and Sonic Datamentioning

confidence: 99%

Close-Range Sensing and Data Fusion for Built Heritage Inspection and Monitoring—A Review

Adamopoulos

Rinaudo

2021

Remote Sensing

View full text Add to dashboard Cite

Built cultural heritage is under constant threat due to environmental pressures, anthropogenic damages, and interventions. Understanding the preservation state of monuments and historical structures, and the factors that alter their architectural and structural characteristics through time, is crucial for ensuring their protection. Therefore, inspection and monitoring techniques are essential for heritage preservation, as they enable knowledge about the altering factors that put built cultural heritage at risk, by recording their immediate effects on monuments and historic structures. Nondestructive evaluations with close-range sensing techniques play a crucial role in monitoring. However, data recorded by different sensors are frequently processed separately, which hinders integrated use, visualization, and interpretation. This article’s aim is twofold: i) to present an overview of close-range sensing techniques frequently applied to evaluate built heritage conditions, and ii) to review the progress made regarding the fusion of multi-sensor data recorded by them. Particular emphasis is given to the integration of data from metric surveying and from recording techniques that are traditionally non-metric. The article attempts to shed light on the problems of the individual and integrated use of image-based modeling, laser scanning, thermography, multispectral imaging, ground penetrating radar, and ultrasonic testing, giving heritage practitioners a point of reference for the successful implementation of multidisciplinary approaches for built cultural heritage scientific investigations.

show abstract

Section: Radar Ultrasonic and Sonic Datamentioning

confidence: 99%

Close-Range Sensing and Data Fusion for Built Heritage Inspection and Monitoring—A Review

Adamopoulos

Rinaudo

2021

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…Since 2014, Microsoft has released Kinect V2 based on ToF. Shortly after the sensor was put into use, C. Beder and M. Stommel respectivity used some simple linear models to describe and correct the errors [3] [4] . Although such method is simple and requires little computation, the correction result is limited.…”

Section: Related Workmentioning

confidence: 99%

A General Means for Depth Data Error Estimation of Depth Sensors

She¹,

Yu²

2019

Proceedings of the 2019 International Conference on Computer, Network, Communication and Information Systems (CNCI 2019)

View full text Add to dashboard Cite

At present the depth error estimation of the RGB-D sensor is aimed at a specific depth camera, the disadvantages of these methods is that it is put forword for a specific sensor and cannot be applied to the other sensors. In order to solve this problem, this paper proposes a general method to estimate the root mean square (RMS) error of depth data provided by general three-dimensional sensors. The method is applicable to three-dimensional sensors based on structured light, time-of-flight, stereo vision and other technologies. Use a common checkerboard to detect corner points and get two point clouds, one is the real point cloud of the image corner, and the other is the estimated point cloud of the corner point given by the device. After registrating of the two point clouds, the RMS error is calculated. The RMS error is generalized as a function of the distance between the RGB-D sensor and the checkerboard .The accuracy and practicability of the proposed method are verified by comparing it with the existing advanced depth error estimation methods.

show abstract