Exterior Orientation Parameters Research Articles

Accuracy analysis of a mobile mapping system for close range photogrammetric projects

Image-based mapping solutions require accurate exterior orientation parameters independently of the cameras used for a survey. This paper analyses the inclusion of up to two stereo-based geometric constraints in the form of baseline distance and convergence angle between camera axes to boost the integrated sensor orientation performance on outdoor close-range projects. A terrestrial low-cost mobile mapping GNSS/IMU multi-camera system is used to test the performance of the stereo-based geometric constraint on a weak geometric network in a stop-and-go survey. The influence of the number of control points (CPs) is analysed to confirm the performance and usability of the geometric constraints in real live terrestrial projects where far from ideal setups can exist across the survey. Improvements in image residuals up to 9 times and deviation errors better than 1 cm are expected when at least three CPs are incorporated into the adjustment.

지오이드 모델에 따른 Boresight 검정 비교

최근 디지털 항공사진측량 분야에서 Direct Georeferencing 활용이 일반화되었으며, 이때 항공사진의 외부표정요소를 정확히 산출하기 위해 Boresight 검정이 필수적이다. 이 과정에서 높이에 대한 기준인 지오이드 모델을 사용하게 되는데, 지오이드 모델에 따라 Boresight 검정 결과가 다르게 나타날 수 있다. 본 연구에서는 Direct Georeferencing을 통한 외부표정요소 산출에 있어서 Boresight 검정시 지오이드 모델에 따라 그 값에 차이가 발생할 수 있음을 인지하고, 지오이드 모델이 Boresight 검정에 어떤 영향을 미치는지 세 가지 경우로 구분하여 실험하였다. 실험에 사용된 지오이드 모델은 EGM96, EGM08, KNGeoid14이며, 이에 따라 Boresight 검정을 통해 Datum Shift, Boresight Angle을 계산하였다. 또한 각각의 경우를 적용해 외부표정요소 산출 후 DPW(Digital Photogrammetry Worktation)를 이용해 지상기준점을 확인하였다. 본 연구를 통해 지오이드 모델에 따른 Boresight 검정 결과 Datum Shift에서 Z에서 차이가 있었으며, 외부표정요소에서 높이 Z와 회전량 Ω, Φ에서 차이를 보였다. 각각의 경우에서 수치도화기를 이용해 지상기준점과 비교한 결과 지오이드 모델에 따른 관측정확도의 차이는 3cm 내외로, 지오이드 모델이 Boresight 검정에 큰 영향을 미치지 않음을 알 수 있었다. Direct georeferencing has become widespread in the field of digital aerial photogrammetry; as a result, the boresight calibration has become an essential component of the procedure to calculating exterior orientation parameters of aerial photographs accurately. During this procedure, a reference is used for the height of the geoid model, and the calibration results can appear different depending on the geoid model. The exterior orientation parameters calculated through direct georeferencing during boresight calibration may have varied values according to the corresponding geoid model. With that in mind, the effects of the geoid model on the boresight calibration were analyzed through three different cases. The geoid models used in the experiments were EGM96, EGM08, and KNGeoid14, and, through boresight calibration, the datum shift and boresight angle for each model was computed. After calculating the exterior orientation of each case, the GCP (Ground Control Point) was verified using the DPW (Digital Photogrammetry Workstation). In each case, results in the boresight calibration acquired through the geoid model demonstrated a difference in the Z datum, the exterior orientation heights Z, and the rotation Ω and Φ. After utilizing the DPW in each case and comparing it to the GCP, the difference in accuracy in accordance with the geoid model was found to be within 3cm, and it was concluded that the geoid model did not have a significant impact on boresight calibration.

Matching Aerial Images to 3D Building Models Using Context-Based Geometric Hashing.

A city is a dynamic entity, which environment is continuously changing over time. Accordingly, its virtual city models also need to be regularly updated to support accurate model-based decisions for various applications, including urban planning, emergency response and autonomous navigation. A concept of continuous city modeling is to progressively reconstruct city models by accommodating their changes recognized in spatio-temporal domain, while preserving unchanged structures. A first critical step for continuous city modeling is to coherently register remotely sensed data taken at different epochs with existing building models. This paper presents a new model-to-image registration method using a context-based geometric hashing (CGH) method to align a single image with existing 3D building models. This model-to-image registration process consists of three steps: (1) feature extraction; (2) similarity measure; and matching, and (3) estimating exterior orientation parameters (EOPs) of a single image. For feature extraction, we propose two types of matching cues: edged corner features representing the saliency of building corner points with associated edges, and contextual relations among the edged corner features within an individual roof. A set of matched corners are found with given proximity measure through geometric hashing, and optimal matches are then finally determined by maximizing the matching cost encoding contextual similarity between matching candidates. Final matched corners are used for adjusting EOPs of the single airborne image by the least square method based on collinearity equations. The result shows that acceptable accuracy of EOPs of a single image can be achievable using the proposed registration approach as an alternative to a labor-intensive manual registration process.

INDOOR POSITIONING AND NAVIGATION BASED ON CONTROL SPHERECAL PANORAMIC IMAGES

Continuous indoor and outdoor positioning and navigation is the goal to achieve in the field of mobile mapping technology. However, accuracy of positioning and navigation will be largely degraded in indoor or occluded areas, due to receiving weak or less GNSS signals. Targeting the need of high accuracy indoor and outdoor positioning and navigation for mobile mapping applications, the objective of this study is to develop a novel method of indoor positioning and navigation with the use of spherical panoramic image (SPI). Two steps are planned in the technology roadmap. First, establishing a control SPI database that contains a good number of well-distributed control SPIs pre-acquired in the target space. A control SPI means an SPI with known exterior orientation parameters, which can be solved with a network bundle adjustment of SPIs. Having a control SPI database, the target space will be ready to provide the service of positioning and navigation. Secondly, the position and orientation of a newly taken SPI can be solved by using overlapped SPIs searched from the control SPI database. The method of matching SPIs and finding conjugate image features will be developed and tested. Two experiments will be planned and conducted in this paper to test the feasibility and validate the test results of the proposed methods. Analysis of appropriate number and distribution of needed control SPIs will also be included in the experiments with respect to different test cases.

INDOOR POSITIONING AND NAVIGATION BASED ON CONTROL SPHERECAL PANORAMIC IMAGES

Abstract. Continuous indoor and outdoor positioning and navigation is the goal to achieve in the field of mobile mapping technology. However, accuracy of positioning and navigation will be largely degraded in indoor or occluded areas, due to receiving weak or less GNSS signals. Targeting the need of high accuracy indoor and outdoor positioning and navigation for mobile mapping applications, the objective of this study is to develop a novel method of indoor positioning and navigation with the use of spherical panoramic image (SPI). Two steps are planned in the technology roadmap. First, establishing a control SPI database that contains a good number of well-distributed control SPIs pre-acquired in the target space. A control SPI means an SPI with known exterior orientation parameters, which can be solved with a network bundle adjustment of SPIs. Having a control SPI database, the target space will be ready to provide the service of positioning and navigation. Secondly, the position and orientation of a newly taken SPI can be solved by using overlapped SPIs searched from the control SPI database. The method of matching SPIs and finding conjugate image features will be developed and tested. Two experiments will be planned and conducted in this paper to test the feasibility and validate the test results of the proposed methods. Analysis of appropriate number and distribution of needed control SPIs will also be included in the experiments with respect to different test cases.

PRICISE TARGET GEOLOCATION AND TRACKING BASED ON UAV VIDEO IMAGERY

Abstract. There is an increasingly large number of applications for Unmanned Aerial Vehicles (UAVs) from monitoring, mapping and target geolocation. However, most of commercial UAVs are equipped with low-cost navigation sensors such as C/A code GPS and a low-cost IMU on board, allowing a positioning accuracy of 5 to 10 meters. This low accuracy cannot be used in applications that require high precision data on cm-level. This paper presents a precise process for geolocation of ground targets based on thermal video imagery acquired by small UAV equipped with RTK GPS. The geolocation data is filtered using an extended Kalman filter, which provides a smoothed estimate of target location and target velocity. The accurate geo-locating of targets during image acquisition is conducted via traditional photogrammetric bundle adjustment equations using accurate exterior parameters achieved by on board IMU and RTK GPS sensors, Kalman filtering and interior orientation parameters of thermal camera from pre-flight laboratory calibration process. The results of this study compared with code-based ordinary GPS, indicate that RTK observation with proposed method shows more than 10 times improvement of accuracy in target geolocation.

PRICISE TARGET GEOLOCATION AND TRACKING BASED ON UAV VIDEO IMAGERY

There is an increasingly large number of applications for Unmanned Aerial Vehicles (UAVs) from monitoring, mapping and target geolocation. However, most of commercial UAVs are equipped with low-cost navigation sensors such as C/A code GPS and a low-cost IMU on board, allowing a positioning accuracy of 5 to 10 meters. This low accuracy cannot be used in applications that require high precision data on cm-level. This paper presents a precise process for geolocation of ground targets based on thermal video imagery acquired by small UAV equipped with RTK GPS. The geolocation data is filtered using an extended Kalman filter, which provides a smoothed estimate of target location and target velocity. The accurate geo-locating of targets during image acquisition is conducted via traditional photogrammetric bundle adjustment equations using accurate exterior parameters achieved by on board IMU and RTK GPS sensors, Kalman filtering and interior orientation parameters of thermal camera from pre-flight laboratory calibration process. The results of this study compared with code-based ordinary GPS, indicate that RTK observation with proposed method shows more than 10 times improvement of accuracy in target geolocation.

BRUTE FORCE MATCHING BETWEEN CAMERA SHOTS AND SYNTHETIC IMAGES FROM POINT CLOUDS

3D point clouds, acquired by state-of-the-art terrestrial laser scanning techniques (TLS), provide spatial information about accuracies up to several millimetres. Unfortunately, common TLS data has no spectral information about the covered scene. However, the matching of TLS data with images is important for monoplotting purposes and point cloud colouration. Well-established methods solve this issue by matching of close range images and point cloud data by fitting optical camera systems on top of laser scanners or rather using ground control points. <br><br> The approach addressed in this paper aims for the matching of 2D image and 3D point cloud data from a freely moving camera within an environment covered by a large 3D point cloud, e.g. a 3D city model. The key advantage of the free movement affects augmented reality applications or real time measurements. Therefore, a so-called real image, captured by a smartphone camera, has to be matched with a so-called synthetic image which consists of reverse projected 3D point cloud data to a synthetic projection centre whose exterior orientation parameters match the parameters of the image, assuming an ideal distortion free camera.

BRUTE FORCE MATCHING BETWEEN CAMERA SHOTS AND SYNTHETIC IMAGES FROM POINT CLOUDS

Abstract. 3D point clouds, acquired by state-of-the-art terrestrial laser scanning techniques (TLS), provide spatial information about accuracies up to several millimetres. Unfortunately, common TLS data has no spectral information about the covered scene. However, the matching of TLS data with images is important for monoplotting purposes and point cloud colouration. Well-established methods solve this issue by matching of close range images and point cloud data by fitting optical camera systems on top of laser scanners or rather using ground control points. The approach addressed in this paper aims for the matching of 2D image and 3D point cloud data from a freely moving camera within an environment covered by a large 3D point cloud, e.g. a 3D city model. The key advantage of the free movement affects augmented reality applications or real time measurements. Therefore, a so-called real image, captured by a smartphone camera, has to be matched with a so-called synthetic image which consists of reverse projected 3D point cloud data to a synthetic projection centre whose exterior orientation parameters match the parameters of the image, assuming an ideal distortion free camera.

THE FEASIBILITY OF 3D POINT CLOUD GENERATION FROM SMARTPHONES

This paper proposes a new technique for increasing the accuracy of direct geo-referenced image-based 3D point cloud generated from low-cost sensors in smartphones. The smartphone’s motion sensors are used to directly acquire the Exterior Orientation Parameters (EOPs) of the captured images. These EOPs, along with the Interior Orientation Parameters (IOPs) of the camera/ phone, are used to reconstruct the image-based 3D point cloud. However, because smartphone motion sensors suffer from poor GPS accuracy, accumulated drift and high signal noise, inaccurate 3D mapping solutions often result. Therefore, horizontal and vertical linear features, visible in each image, are extracted and used as constraints in the bundle adjustment procedure. These constraints correct the relative position and orientation of the 3D mapping solution. Once the enhanced EOPs are estimated, the semi-global matching algorithm (SGM) is used to generate the image-based dense 3D point cloud. Statistical analysis and assessment are implemented herein, in order to demonstrate the feasibility of 3D point cloud generation from the consumer-grade sensors in smartphones.

THE FEASIBILITY OF 3D POINT CLOUD GENERATION FROM SMARTPHONES

Abstract. This paper proposes a new technique for increasing the accuracy of direct geo-referenced image-based 3D point cloud generated from low-cost sensors in smartphones. The smartphone’s motion sensors are used to directly acquire the Exterior Orientation Parameters (EOPs) of the captured images. These EOPs, along with the Interior Orientation Parameters (IOPs) of the camera/ phone, are used to reconstruct the image-based 3D point cloud. However, because smartphone motion sensors suffer from poor GPS accuracy, accumulated drift and high signal noise, inaccurate 3D mapping solutions often result. Therefore, horizontal and vertical linear features, visible in each image, are extracted and used as constraints in the bundle adjustment procedure. These constraints correct the relative position and orientation of the 3D mapping solution. Once the enhanced EOPs are estimated, the semi-global matching algorithm (SGM) is used to generate the image-based dense 3D point cloud. Statistical analysis and assessment are implemented herein, in order to demonstrate the feasibility of 3D point cloud generation from the consumer-grade sensors in smartphones.

GEOMETRIC CALIBRATION OF THE CLEMENTINE UVVIS CAMERA USING IMAGES ACQUIRED BY THE LUNAR RECONNAISSANCE ORBITER

Abstract. The Clementine UVVIS camera returned over half a million images while in orbit around the Moon in 1994. Since the Clementine mission, our knowledge of lunar topography, gravity, and the location of features on the surface has vastly improved with the success of the Gravity Recovery and Interior Laboratory (GRAIL) mission and ongoing Lunar Reconnaissance Orbiter (LRO) mission. In particular, the Lunar Reconnaissance Orbiter Camera (LROC) has returned over a million images of the Moon since entering orbit in 2009. With the aid of improved ephemeris and on-orbit calibration, the LROC team created a series of precise and accurate global maps. With the updated reference frame, older lunar maps, such as those generated from Clementine UVVIS images, are misaligned making cross-mission analysis difficult. In this study, we use feature-based matching routines to refine and recalibrate the interior and exterior orientation parameters of the Clementine UVVIS camera. After applying these updates and rigorous orthorectification, we are able generate precise and accurate maps from UVVIS images to help support lunar science and future cross-mission investigations.

GEOMETRIC CALIBRATION OF THE CLEMENTINE UVVIS CAMERA USING IMAGES ACQUIRED BY THE LUNAR RECONNAISSANCE ORBITER

The Clementine UVVIS camera returned over half a million images while in orbit around the Moon in 1994. Since the Clementine mission, our knowledge of lunar topography, gravity, and the location of features on the surface has vastly improved with the success of the Gravity Recovery and Interior Laboratory (GRAIL) mission and ongoing Lunar Reconnaissance Orbiter (LRO) mission. In particular, the Lunar Reconnaissance Orbiter Camera (LROC) has returned over a million images of the Moon since entering orbit in 2009. With the aid of improved ephemeris and on-orbit calibration, the LROC team created a series of precise and accurate global maps. With the updated reference frame, older lunar maps, such as those generated from Clementine UVVIS images, are misaligned making cross-mission analysis difficult. In this study, we use feature-based matching routines to refine and recalibrate the interior and exterior orientation parameters of the Clementine UVVIS camera. After applying these updates and rigorous orthorectification, we are able generate precise and accurate maps from UVVIS images to help support lunar science and future cross-mission investigations.

GLOBAL BUNDLE ADJUSTMENT WITH VARIABLE ORIENTATION POINT DISTANCE FOR PRECISE MARS EXPRESS ORBIT RECONSTRUCTION

Abstract. The photogrammetric bundle adjustment of line scanner image data requires a precise description of the time-dependent image orientation. For this task exterior orientation parameters of discrete points are used to model position and viewing direction of a camera trajectory via polynomials. This paper investigates the influence of the distance between these orientation points on the quality of trajectory modeling. A new method adapts the distance along the trajectory to the available image information. Compared to a constant distance as used previously, a better reconstruction of the exterior orientation is possible, especially when image quality changes within a strip. In our research we use image strips of the High Resolution Stereo Camera (HRSC), taken to map the Martian surface. Several experiments on the global image data set have been carried out to investigate how the bundle adjustment improves the image orientation, if the new method is employed. For evaluation the forward intersection errors of 3D points derived from HRSC images, as well as their remaining height differences to the MOLA DTM are used. In 13.5 % (515 of 3,828) of the image strips, taken during this ongoing mission over the last 12 years, high frequency image distortions were found. Bundle adjustment with a constant orientation point distance was able to reconstruct the orbit in 239 (46.4 %) cases. A variable orientation point distance increased this number to 507 (98.6 %).

GLOBAL BUNDLE ADJUSTMENT WITH VARIABLE ORIENTATION POINT DISTANCE FOR PRECISE MARS EXPRESS ORBIT RECONSTRUCTION

The photogrammetric bundle adjustment of line scanner image data requires a precise description of the time-dependent image orientation. For this task exterior orientation parameters of discrete points are used to model position and viewing direction of a camera trajectory via polynomials. This paper investigates the influence of the distance between these orientation points on the quality of trajectory modeling. A new method adapts the distance along the trajectory to the available image information. Compared to a constant distance as used previously, a better reconstruction of the exterior orientation is possible, especially when image quality changes within a strip. <br><br> In our research we use image strips of the High Resolution Stereo Camera (HRSC), taken to map the Martian surface. Several experiments on the global image data set have been carried out to investigate how the bundle adjustment improves the image orientation, if the new method is employed. For evaluation the forward intersection errors of 3D points derived from HRSC images, as well as their remaining height differences to the MOLA DTM are used. <br><br> In 13.5 % (515 of 3,828) of the image strips, taken during this ongoing mission over the last 12 years, high frequency image distortions were found. Bundle adjustment with a constant orientation point distance was able to reconstruct the orbit in 239 (46.4 %) cases. A variable orientation point distance increased this number to 507 (98.6 %).

APPLICATION OF VEHICLE DYNAMIC MODELING IN UAVS FOR PRECISE DETERMINATION OF EXTERIOR ORIENTATION

Advances in unmanned aerial vehicles (UAV) and especially micro aerial vehicle (MAV) technology together with increasing quality and decreasing price of imaging devices have resulted in growing use of MAVs in photogrammetry. The practicality of MAV mapping is seriously enhanced with the ability to determine parameters of exterior orientation (EO) with sufficient accuracy, in both absolute and relative senses (change of attitude between successive images). While differential carrier phase GNSS satisfies cm-level positioning accuracy, precise attitude determination is essential for both direct sensor orientation (DiSO) and integrated sensor orientation (ISO) in corridor mapping or in block configuration imaging over surfaces with low texture. Limited cost, size, and weight of MAVs represent limitations on quality of onboard navigation sensors and puts emphasis on exploiting full capacity of available resources. Typically short flying times (10-30 minutes) also limit the possibility of estimating and/or correcting factors such as sensor misalignment and poor attitude initialization of inertial navigation system (INS). This research aims at increasing the accuracy of attitude determination in both absolute and relative senses with no extra sensors onboard. In comparison to classical INS/GNSS setup, novel approach is presented here to integrated state estimation, in which vehicle dynamic model (VDM) is used as the main process model. Such system benefits from available information from autopilot and physical properties of the platform in enhancing performance of determination of trajectory and parameters of exterior orientation consequently. The navigation system employs a differential carrier phase GNSS receiver and a micro electro-mechanical system (MEMS) grade inertial measurement unit (IMU), together with MAV control input from autopilot. Monte-Carlo simulation has been performed on trajectories for typical corridor mapping and block imaging. Results reveal considerable reduction in attitude errors with respect to conventional INS/GNSS system, in both absolute and relative senses. This eventually translates into higher redundancy and accuracy for photogrammetry applications.

APPLICATION OF VEHICLE DYNAMIC MODELING IN UAVS FOR PRECISE DETERMINATION OF EXTERIOR ORIENTATION

Abstract. Advances in unmanned aerial vehicles (UAV) and especially micro aerial vehicle (MAV) technology together with increasing quality and decreasing price of imaging devices have resulted in growing use of MAVs in photogrammetry. The practicality of MAV mapping is seriously enhanced with the ability to determine parameters of exterior orientation (EO) with sufficient accuracy, in both absolute and relative senses (change of attitude between successive images). While differential carrier phase GNSS satisfies cm-level positioning accuracy, precise attitude determination is essential for both direct sensor orientation (DiSO) and integrated sensor orientation (ISO) in corridor mapping or in block configuration imaging over surfaces with low texture. Limited cost, size, and weight of MAVs represent limitations on quality of onboard navigation sensors and puts emphasis on exploiting full capacity of available resources. Typically short flying times (10-30 minutes) also limit the possibility of estimating and/or correcting factors such as sensor misalignment and poor attitude initialization of inertial navigation system (INS). This research aims at increasing the accuracy of attitude determination in both absolute and relative senses with no extra sensors onboard. In comparison to classical INS/GNSS setup, novel approach is presented here to integrated state estimation, in which vehicle dynamic model (VDM) is used as the main process model. Such system benefits from available information from autopilot and physical properties of the platform in enhancing performance of determination of trajectory and parameters of exterior orientation consequently. The navigation system employs a differential carrier phase GNSS receiver and a micro electro-mechanical system (MEMS) grade inertial measurement unit (IMU), together with MAV control input from autopilot. Monte-Carlo simulation has been performed on trajectories for typical corridor mapping and block imaging. Results reveal considerable reduction in attitude errors with respect to conventional INS/GNSS system, in both absolute and relative senses. This eventually translates into higher redundancy and accuracy for photogrammetry applications.

EXTERIOR ORIENTATION ESTIMATION OF OBLIQUE AERIAL IMAGERY USING VANISHING POINTS

In this paper, a methodology for the calculation of rough exterior orientation (EO) parameters of multiple large-scale overlapping oblique aerial images, in the case that GPS/INS information is not available (e.g., for old datasets), is presented. It consists of five main steps; (a) the determination of the overlapping image pairs and the single image in which four ground control points have to be measured; (b) the computation of the transformation parameters from every image to the coordinate reference system; (c) the rough estimation of the camera interior orientation parameters; (d) the estimation of the true horizon line and the nadir point of each image; (e) the calculation of the rough EO parameters of each image. A developed software suite implementing the proposed methodology is tested using a set of UAV multi-perspective oblique aerial images. Several tests are performed for the assessment of the errors and show that the estimated EO parameters can be used either as initial approximations for a bundle adjustment procedure or as rough georeferencing information for several applications, like 3D modelling, even by non-photogrammetrists, because of the minimal user intervention needed. Finally, comparisons with a commercial software are made, in terms of automation and correctness of the computed EO parameters.

EXTERIOR ORIENTATION ESTIMATION OF OBLIQUE AERIAL IMAGERY USING VANISHING POINTS

Abstract. In this paper, a methodology for the calculation of rough exterior orientation (EO) parameters of multiple large-scale overlapping oblique aerial images, in the case that GPS/INS information is not available (e.g., for old datasets), is presented. It consists of five main steps; (a) the determination of the overlapping image pairs and the single image in which four ground control points have to be measured; (b) the computation of the transformation parameters from every image to the coordinate reference system; (c) the rough estimation of the camera interior orientation parameters; (d) the estimation of the true horizon line and the nadir point of each image; (e) the calculation of the rough EO parameters of each image. A developed software suite implementing the proposed methodology is tested using a set of UAV multi-perspective oblique aerial images. Several tests are performed for the assessment of the errors and show that the estimated EO parameters can be used either as initial approximations for a bundle adjustment procedure or as rough georeferencing information for several applications, like 3D modelling, even by non-photogrammetrists, because of the minimal user intervention needed. Finally, comparisons with a commercial software are made, in terms of automation and correctness of the computed EO parameters.

DUBAI 3D TEXTUERD MESH USING HIGH QUALITY RESOLUTION VERTICAL/OBLIQUE AERIAL IMAGERY

Abstract. Providing high quality 3D data with reasonable quality and cost were always essential, affording the core data and foundation for developing an information-based decision-making tool of urban environments with the capability of providing decision makers, stakeholders, professionals, and public users with 3D views and 3D analysis tools of spatial information that enables real-world views. Helps and assist in improving users’ orientation and also increase their efficiency in performing their tasks related to city planning, Inspection, infrastructures, roads, and cadastre management. In this paper, the capability of multi-view Vexcel UltraCam Osprey camera images is examined to provide a 3D model of building façades using an efficient image-based modeling workflow adopted by commercial software’s. The main steps of this work include: Specification, point cloud generation, and 3D modeling. After improving the initial values of interior and exterior parameters at first step, an efficient image matching technique such as Semi Global Matching (SGM) is applied on the images to generate point cloud. Then, a mesh model of points is calculated using and refined to obtain an accurate model of buildings. Finally, a texture is assigned to mesh in order to create a realistic 3D model. The resulting model has provided enough LoD2 details of the building based on visual assessment. The objective of this paper is neither comparing nor promoting a specific technique over the other and does not mean to promote a sensor-based system over another systems or mechanism presented in existing or previous paper. The idea is to share experience.