This paper examines the potential of mobile phones to be used as front-end sensors for photogrammetric procedures and applications. For this purpose, two mobile phone cameras (Sony Ericsson K750i and Nokia N93) were calibrated over an indoor 3D testfield, using a self-calibrating bundle adjustment. Geometric accuracy tests were carried out in order to evaluate their metric performances and to compare the results with respect to two off-the-shelf digital still video cameras (Sony DSC W100 and Sony DSC F828). The geometric accuracy evaluation comprised an absolute accuracy test, JPEG test and temporal stability test. The radiometric capabilities of all cameras (except that the DSC W100 was replaced with a DSC T100 camera) were also evaluated and compared by carrying out modulation transfer function (MTF) analysis, image noise analysis and an operating range test. Substantial systematic errors were diagnosed in some systems. However, with proper calibration it is believed that these devices can be used for many photogrammetric tasks.

/pdf/comparative-geometric-and-radiometric-evaluation-of-mobile-4ix5xqh8op.pdf

Comparative geometric and radiometric evaluation of mobile phone and still video cameras

3D accident reconstruction using low-cost imaging technique

3D scene classification has become an important research field in photogrammetry, remote sensing, computer vision and robotics with the widespread usage of 3D point clouds. Point cloud classification, called semantic labeling, semantic segmentation, or semantic classification of point clouds is a challenging topic. Machine learning, on the other hand, is a powerful mathematical tool used to classify 3D point clouds whose content can be significantly complex. In this study, the classification performance of different machine learning algorithms in multiple scales was evaluated. The feature spaces of the points in the point cloud were created using the geometric features generated based on the eigenvalues of the covariance matrix. Eight supervised classification algorithms were tested in four different areas from three datasets (the Dublin City dataset, Vaihingen dataset and Oakland3D dataset). The algorithms were evaluated in terms of overall accuracy, precision, recall, F1 score and process time. The best overall results were obtained for four test areas with different algorithms. Dublin City Area 1 was obtained with Random Forest as 93.12%, Dublin City Area 2 was obtained with a Multilayer Perceptron algorithm as 92.78%, Vaihingen was obtained as 79.71% with Support Vector Machines and Oakland3D with Linear Discriminant Analysis as 97.30%.

Machine Learning-Based Supervised Classification of Point Clouds Using Multiscale Geometric Features

3D data is the main element of three-dimensional models that are commonly used in many different application areas such as architecture, archeology, medicine, film production, reverse engineering, etc. In this study, we discuss designing and utilizing opportunities for a low-cost triangulation-based laser scanner. To evaluate the system, a test object is specially designed, and the accuracy and operation of the system are verified by comparing the data output of the laser scanner with the terrestrial photogrammetric method. The designed system is also verified using different objects. At the end of the study, we calculate the precision of the scanned sample models to be 0.1–0.2 mm and RMS, 0.59 mm.

Low-Cost Laser Scanning System Design

Solar energy is a renewable energy source directly from sunlight and its production depends on roof characteristics such as roof type and size. In solar potential analysis, the main purpose is to determine the suitable roofs for the placement of solar panels. Hence, roof plane detection plays a crucial role in solar energy assessment. In this study, a detailed comparison was presented between aerial photogrammetry data and LIDAR data for roof plane recognition applying RANSAC (Random Sample Consensus) algorithm. RANSAC algorithm was performed to 3D-point clouds obtained by both LIDAR (Laser Ranging and Detection) and aerial photogrammetric survey. In this regard, solar energy assessment from the results can be applied. It is shown that, the RANSAC algorithm detects building roofs better on the point cloud data acquired from airborne LIDAR regarding completeness within model, since aerial photogrammetric survey provides noisy data in spite of its high-density data. This noise in the source data leads to deformations in roof plane detection. The study area of the project is the campus of Istanbul Technical University. Accuracy information of the roof extraction of three different buildings are presented in tables.

/pdf/extraction-of-roof-planes-from-different-point-clouds-using-1sek3xfgd5.pdf

Extraction of Roof Planes from Different Point Clouds Using RANSAC Algorithm

Digital modeling of world's first known length reference unit: The Nippur cubit rod

In this study, a limestone rock core specimen with 6.94 cm x 4.95 cm dimensions was exposed to tensile force by Brazilian test and rough surfaces were obtained. Following the Brazilian test, roughness angles were measured by a laser scanner along one side of the rock specimen. For this purpose, Nextengine 3D Desktop scanner was used. The 17 profiles were studied along the width of the core with a 0.3 mm interval. Approximately 10000 points produced for each profile, some of them are in the “+” and some are in the “-” direction along each profile. Maximum and minimum roughness angles are calculated as 65.580 and 1.56x10-5 degree respectively. The average roughness angle value of the profiles is 13.870. The percentage of the roughness angle between 13 and 14 degrees were 2.65% and 2.70% for “-” and “+” directions on the rock surface, respectively. Mathematical analyses of 17 profiles showed that roughness profiles can be expressed by 21st – 30th degree polynomial equations with approximately 10-4 degree standard deviation.

/pdf/determining-roughness-angle-of-limestone-using-optical-laser-asp7n8ile5.pdf

Determining Roughness Angle of Limestone Using Optical Laser Scanner

Iconography : Digital modeling of world’s first known length reference unit: The Nippur cubit rod

Turkeys National Cadastres Istanbul Branch Office Building is one of the most impressive buildings of Historical Peninsula. It is located in the Sultan Ahmet Square, in Istanbul. In the near vicinity of this building, there are several historical buildings, like the Hagia Sophia, which is registered as a World Heritage by UNESCO and Sultan Ahmet Mosque known as Blue Mosque. The building consists of two parts. First part was built in 1881 and still used for storing of the Ottoman Archive. Second part was built in 1910 by the Architect Vedat Mehmet Efendi and merged with the first part. The area of the facade of the building is 850m 2 . In this application, digital images of this office building have been taken from different sources such as mobile phone and digital camera. Then these images were rectified using Pictran, Photomodeler and PhoTopol. The aim of this study is to compare the image sources regarding the capability of using them for various purposes and the capacity and usability of the software’s regarding manpower, obtained results and transfer to other visualization tools.

https://www.isprs.org/proceedings/XXXVI/5-C53/papers/FP018.pdf

Obtaining façade plan of a historical building with orthorectification of single images gathered by mobile phone and digital camera

Co-registration of point clouds of partially scanned objects is the first step of the 3D modeling workflow. The aim of coregistration is to merge the overlapping point clouds by estimating the spatial transformation parameters. In computer vision and photogrammetry domain one of the most popular methods is the ICP (Iterative Closest Point) algorithm and its variants. There exist the 3D Least Squares (LS) matching methods as well (Gruen and Akca, 2005). The co-registration methods commonly use the least squares (LS) estimation method in which the unknown transformation parameters of the (floating) search surface is functionally related to the observation of the (fixed) template surface. Here, the stochastic properties of the search surfaces are usually omitted. This omission is expected to be minor and does not disturb the solution vector significantly. However, the a posteriori covariance matrix will be affected by the neglected uncertainty of the function values of the search surface. . This causes deterioration in the realistic precision estimates. In order to overcome this limitation, we propose a method where the stochastic properties of both the observations and the parameters are considered under an errors-in-variables (EIV) model. The experiments have been carried out using diverse laser scanning data sets and the results of EIV with the ICP and the conventional LS matching methods have been compared.

/pdf/total-least-squares-registration-of-3d-surfaces-242sqfdqsm.pdf

Umut Aydar

Papers

Digital modeling of world's first known length reference unit: The Nippur cubit rod

Determining Roughness Angle of Limestone Using Optical Laser Scanner

Iconography : Digital modeling of world’s first known length reference unit: The Nippur cubit rod

Obtaining façade plan of a historical building with orthorectification of single images gathered by mobile phone and digital camera

Total Least Squares Registration of 3D Surfaces