Terrestrial Images Research Articles

VIRTUAL 3D CITY MODELING: TECHNIQUES AND APPLICATIONS

Abstract. 3D city model is a digital representation of the Earth's surface and it's related objects such as Building, Tree, Vegetation, and some manmade feature belonging to urban area. There are various terms used for 3D city models such as "Cybertown", "Cybercity", "Virtual City", or "Digital City". 3D city models are basically a computerized or digital model of a city contains the graphic representation of buildings and other objects in 2.5 or 3D. Generally three main Geomatics approach are using for Virtual 3-D City models generation, in first approach, researcher are using Conventional techniques such as Vector Map data, DEM, Aerial images, second approach are based on High resolution satellite images with LASER scanning, In third method, many researcher are using Terrestrial images by using Close Range Photogrammetry with DSM & Texture mapping. We start this paper from the introduction of various Geomatics techniques for 3D City modeling. These techniques divided in to two main categories: one is based on Automation (Automatic, Semi-automatic and Manual methods), and another is Based on Data input techniques (one is Photogrammetry, another is Laser Techniques). After details study of this, finally in short, we are trying to give the conclusions of this study. In the last, we are trying to give the conclusions of this research paper and also giving a short view for justification and analysis, and present trend for 3D City modeling. This paper gives an overview about the Techniques related with "Generation of Virtual 3-D City models using Geomatics Techniques" and the Applications of Virtual 3D City models. Photogrammetry, (Close range, Aerial, Satellite), Lasergrammetry, GPS, or combination of these modern Geomatics techniques play a major role to create a virtual 3-D City model. Each and every techniques and method has some advantages and some drawbacks. Point cloud model is a modern trend for virtual 3-D city model. Photo-realistic, Scalable, Geo-referenced virtual 3-D City model is a very useful for various kinds of applications such as for planning in Navigation, Tourism, Disasters Management, Transportations, Municipality, Urban Environmental Managements and Real-estate industry. So the Construction of Virtual 3-D city models is a most interesting research topic in recent years.

JOINT PROCESSING OF UAV IMAGERY AND TERRESTRIAL MOBILE MAPPING SYSTEM DATA FOR VERY HIGH RESOLUTION CITY MODELING

Abstract. Both unmanned aerial vehicle (UAV) technology and Mobile Mapping Systems (MMS) are important techniques for surveying and mapping. In recent years, the UAV technology has seen tremendous interest, both in the mapping community and in many other fields of application. Carrying off-the shelf digital cameras, the UAV can collect high quality aerial optical images for city modeling using photogrammetric techniques. In addition, a MMS can acquire high density point clouds of ground objects along the roads. The UAV, if operated in an aerial mode, has difficulties in acquiring information of ground objects under the trees and along façades of buildings. On the contrary, the MMS collects accurate point clouds of objects from the ground, together with stereo images, but it suffers from system errors due to loss of GPS signals, and also lacks the information of the roofs. Therefore, both technologies are complementary. This paper focuses on the integration of UAV images, MMS point cloud data and terrestrial images to build very high resolution 3D city models. The work we will show is a practical modeling project of the National University of Singapore (NUS) campus, which includes buildings, some of them very high, roads and other man-made objects, dense tropical vegetation and DTM. This is an intermediate report. We present work in progress.

Photogrammetric Techniques for the Determination of Spatio-temporal Velocity Fields at Glaciar San Rafael, Chile

Glaciar San Rafael in the Northern Patagonia Icefield, with a length of 46 km and an ice area of 722 km2, is the lowest latitude tidewater outlet glacier in the world and one of the fastest and most productive glaciers in southern South America in terms of iceberg flux. Spatio-temporal velocity fields in the region of the glacier front were determined from monoscopic terrestrial image sequences recorded by an inter-vallometer mode high-resolution digital camera over several days. In these image sequences, a large number of glacier surface points were tracked by subpixel accuracy feature tracking techniques. Scaling and georeferencing of the trajectories obtained from image space tracking was performed using a multi-station GPS-supported photogrammetric network. The technique allows for tracking hundreds of glacier surface points at a measurement accuracy in the order of one decimeter, for typical glacier movement rates, and an almost arbitrarily high temporal resolution. The results show velocities of up to 16 meters per day.

A Synergistic Approach for Recovering Occlusion-Free Textured 3D Maps of Urban Facades from Heterogeneous Cartographic Data

In this paper we present a practical approach for generating an occlusion-free textured 3D map of urban facades by the synergistic use of terrestrial images, 3D point clouds and area-based information. Particularly in dense urban environments, the high presence of urban objects in front of the facades causes significant difficulties for several stages in computational building modeling. Major challenges lie on the one hand in extracting complete 3D facade quadrilateral delimitations and on the other hand in generating occlusion-free facade textures. For these reasons, we describe a straightforward approach for completing and recovering facade geometry and textures by exploiting the data complementarity of terrestrial multi-source imagery and area-based information.

UAV PHOTOGRAMMETRY IN REMOTE AREAS – 3D MODELING OF DRAPHAM DZONG BHUTAN

Abstract. The Buddhist fortress Drapham Dzong is located in the Bumthang District in central Bhutan on a mountain ridge at approximately 2930 m altitude. A surveying mission was carried out with the goal to produce a textured 3D model of the site, together with a conventional 2D map and an orthophoto. The remote and exposed location of the site makes traditional surveying, including terrestrial laser-scanning a tedious undertaking. Aerial images of reasonable quality are not available in Bhutan. Satellite images, even of very high resolution (50 cm footprint) do not show sufficient detail for the modelling of the man-made structures. Therefore this project leads itself very well to the use of an appropriate UAV. This paper describes briefly the raw data acquisition by UAV and terrestrial images, and then focuses on the procedures for data processing. The 3D model of the man-made objects is embedded into a large area model of the environment, generated from a GeoEye-1 stereo model. Overall, the project is a typical example of a multi-image concept, making use of imagery of quite different resolutions (satellite, UAV, terrestrial). Given a textured accurate 3D model, 2D maps and orthoimages are nothing but derivatives of this general product. The project also shows how lightweight equipment can be used in remote and not easily accessible areas for surveying and 3D modelling purposes.

ORIENTATION AND DENSE RECONSTRUCTION OF UNORDERED TERRESTRIAL AND AERIAL WIDE BASELINE IMAGE SETS

Abstract. In this paper we present an approach for detailed and precise automatic dense 3D reconstruction using images from consumer cameras. The major difference between our approach and many others is that we focus on wide-baseline image sets. We have combined and improved several methods, particularly, least squares matching, RANSAC, scale-space maxima and bundle adjustment, for robust matching and parameter estimation. Point correspondences and the five-point algorithm lead to relative orientation. Due to our robust matching method it is possible to orient images under much more unfavorable conditions, for instance concerning illumination changes or scale differences, than for often used operators such as SIFT. For dense reconstruction, we use our orientation as input for Semiglobal Matching (SGM) resulting into dense depth images. The latter can be fused into a 2.5D model for eliminating the redundancy of the highly overlapping depth images. However, some applications require full 3D models. A solution to this problem is part of our current work, for which preliminary results are presented in this paper. With very small unmanned aerial systems (Micro UAS) it is possible to acquire images which have a perspective similar to terrestrial images and can thus be combined with them. Such a combination is useful for an almost complete 3D reconstruction of urban scenes. We have applied our approach to several hundred aerial and terrestrial images and have generated detailed 2.5D and 3D models of urban areas.

High-Precision 3D Modeling Method Based on Terrestrial Image Sequences for Alpine-Gorge Area

The traditional measurement methods cannot adapt to the arduous topography of alpine-gorge area. Aiming at the topographical features of alpine-gorge area, we will introduce a general terrestrial method of multi-baseline photogrammetry basing on digital camera here, and then the paper mainly studies the metrization method of common digital camera and matching method of the digital image sequences of alpine-gorge area. Through the metrization of common digital camera, the efficiency of terrain data collection will increase in the alpine-gorge area, and the requirements of operations on the image control and algorithm will reduce. The combination of seed points and multiple constraints in multi-baseline stereo matching will help to solve many problems, such as shading, severe distortion between the left image and the right one, and the inconformity of scale. The modeling process stated above is quite fast and highly precise, and the three-dimensional modeling experiments show that the relative accuracy can reach from 1 / 8000 to 1 / 12000.

Integrated point and edge matching on poor textural images constrained by self-adaptive triangulations

This paper presents an innovative image matching method for reliable and dense image matching on poor textural images, which is the integrated point and edge matching based on the self-adaptive edge-constrained triangulations. Firstly, several seed points and seed edges are obtained on the stereo images, and they are used to construct a pair of initial edge-constrained triangulations on the images. Then, points and edges are matched based on the triangle constraint and other constraints. The newly matched points and edges are inserted into the triangulations and the constrained triangulations are updated dynamically along with the matching propagation. The final results will be the final edge-constrained triangulations generated from the successfully matched points and edges. Experiments using typical space-borne, airborne, and terrestrial images with poor textures revealed that the integrated point and edge matching method based on self-adaptive triangulations is able to produce dense and reliable matching results. Moreover, from the final matched points and edges, 3D points and edges preserving the physical boundaries of objects can be further derived based on photogrammetric techniques, which is ideal for further object modeling applications.

A Study of the characteristic of the observation angle on the terrestrial image measurement of paddy vegetation cover

Vegetation coverage is one of the well-known parameters indicating the growth of rice plants. It is usually calculated as a ratio of the extracted visible green area of plants, the green visibility ratio, in a camera image using the vertical photography. However, early in the season, growth is mostly vertical and therefore a near-vertical observation angle doesn't observe vegetation coverage sensitively. But, a near-horizontal angle of observation can observe vertical and horizontal growth and allow early season monitoring of vegetation coverage. In this paper, the green visibility ratios were calculated by the visible green areas in different observation angles (the range of the observation angle 0.0 degrees to 22.0 degrees) using camera image data taken from vertical photography during the growing period. The relationship between the green visibility ratio and the observation angle was analyzed. As a result of this analysis, the green visibility ratio was increased with the increase in the observation angle and the according to the progress of growth. In addition, the range of the observation angle with the green visibility ratio almost equivalent as the vegetation coverage was decreased rapidly with the progress of growth in 40 to 50 days after transplanting.

Recovering Occlusion-Free Textured 3D Maps of Urban Facades by a Synergistic Use of Terrestrial Images, 3D Point Clouds and Area-Based Information

In this paper we present a practical approach for generating an occlusion-free textured 3D map of urban facades by the synergistic use of terrestrial images, 3D point clouds and area-based information. Particularly in dense urban environments, the high presence of urban objects in front of the facades causes significant difficulties for several stages in computational building modeling. Major challenges lie on the one hand in extracting complete 3D facade quadrilateral delimitations and on the other hand in generating occlusion-free facade textures. For these reasons, we describe a straightforward approach for completing and recovering facade geometry and textures by exploiting the data complementarity of terrestrial multi-source imagery and area-based information.

Development of a 3D Information System for the Old City Centre of Athens

The representation of three dimensional city models has been gaining ground increasingly in many scientific fields in the recent years. 3D City Modelling is a scale representation of natural and artificial objects in order to present the spatial data and highlight the social development of the city. Depending on its importance or the purpose of use, an object can be represented in various levels of detail. An increasing tendency to 3D city models is their integration into GIS, which proves to be an effective tool for managing, analyzing and planning in order to make decisions about technical, administrative and financial matters. A combination of digital photogrammetric techniques and laser scanning data contribute greatly to this, since a variety of data, such as aerial, satellite and terrestrial images, point clouds from airborne and terrestrial laser systems, and also a variety of photogrammetric and mobile mapping methods are available. The objective of this paper is the development of a 3D Information System (IS) for the three-dimensional geometric documentation of the buildings owned by the Ministry of Culture in the old city centre of Athens, Greece, named “Plaka”. The area has been inhabited continuously since the prehistoric era, it has a special architectural style and includes a number of unique cultural heritage monuments. The data used for the reconstruction of the 3D model of Plaka consisted of aerial and terrestrial images, while raster, vector and descriptive data were used for the creation of a 2D GIS, which served as the background for the development of the 3D GIS. The latter includes all of the qualitative and quantitative information related to the 3D building models owned by the Ministry of Culture according to users’ needs. Each building in the vicinity of Plaka was depicted in one of the four different levels of detail created for the purpose of the study, according to their ownership status and other criteria. The building models, depicted in the highest level of detail, were owned by the Greek Ministry of Culture whereas the other buildings (of a lower level of detail) were depicted in a more subtractive way. Therefor an integrated IS was developed that combines descriptive information, e.g., use, legal status, images, drawings, etc, with the spatial information and geometric documentation in three dimensions.

Photorealistic Building Reconstruction from Mobile Laser Scanning Data

Nowadays, advanced real-time visualization for location-based applications, such as vehicle navigation or mobile phone navigation, requires large scale 3D reconstruction of street scenes. This paper presents methods for generating photorealistic 3D city models from raw mobile laser scanning data, which only contain georeferenced XYZ coordinates of points, to enable the use of photorealistic models in a mobile phone for personal navigation. The main focus is on the automated processing algorithms for noise point filtering, ground and building point classification, detection of planar surfaces, and on the key points (e.g., corners) of building derivation. The test site is located in the Tapiola area, Espoo, Finland. It is an area of commercial buildings, including shopping centers, banks, government agencies, bookstores, and high-rise residential buildings, with the tallest building being 45 m in height. Buildings were extracted by comparing the overlaps of X and Y coordinates of the point clouds between the cutoff-boxes at different and transforming the top-view of the point clouds of each overlap into a binary image and applying standard image processing technology to remove the non-building points, and finally transforming this image back into point clouds. The purpose for using points from cutoff-boxes instead of all points for building detection is to reduce the influence of tree points close to the building facades on building extraction. This method can also be extended to transform point clouds in different views into binary images for various other object extractions. In order to ensure the building geometry completeness, manual check and correction are needed after the key points of building derivation by automated algorithms. As our goal is to obtain photorealistic 3D models for walk-through views, terrestrial images were captured and used for texturing building facades. Currently, fully automatic generation of high quality 3D models is still challenging due to occlusions in both the laser and image data and due to significant illumination changes between the images. Especially when the scene contains both trees and vehicles, fully automated methods cannot achieve satisfactory visual appearance. In our approach, we employed the existing software for texture preparation and mapping.

A New Approach for 3D Range Image Segmentation using Gradient Method

Problem statement: Segmentation of 3D range images is widely used in computer vision as an essential pre-processing step before the methods of high-level vision can be applied. Segmentation aims to study and recognize the features of range image such as 3D edges, connected surfaces and smooth regions. Approach: This study presents new improvements in segmentation of terrestrial 3D range images based on edge detection technique. The main idea is to apply a gradient edge detector in three different directions of the 3D range images. This 3D gradient detector is a generalization of the classical sobel operator used with 2D images, which is based on the differences of normal vectors or geometric locations in the coordinate directions. The proposed algorithm uses a 3D-grid structure method to handle large amount of unordered sets of points and determine neighborhood points. It segments the 3D range images directly using gradient edge detectors without any further computations like mesh generation. Our algorithm focuses on extracting important linear structures such as doors, stairs and windows from terrestrial 3D range images these structures are common in indoors and outdoors in many environments. Results: Experimental results showed that the proposed algorithm provides a new approach of 3D range image segmentation with the characteristics of low computational complexity and less sensitivity to noise. The algorithm is validated using seven artificially generated datasets and two real world datasets. Conclusion/Recommendations: Experimental results showed that different segmentation accuracy is achieved by using higher Grid resolution and adaptive threshold.

Orientation and 3D modelling from markerless terrestrial images: combining accuracy with automation

Abstract In this paper an automated methodology is presented (i) to orient a set of close‐range images captured with a calibrated camera, and (ii) to extract dense and accurate point clouds starting from the estimated orientation parameters. The whole procedure combines different algorithms and techniques in order to obtain accurate 3D reconstructions in an automatic way. The exterior orientation parameters are estimated using a photogrammetric bundle adjustment with the image correspondences detected using area‐ and feature‐based matching algorithms. Surface measurements are then performed using advanced multi‐image matching techniques based on multiple image primitives. To demonstrate the reliability, precision and robustness of the procedure, several tests on different kinds of free‐form objects are illustrated and discussed in the paper. Three‐dimensional comparisons with range‐based data are also carried out.

Registration of Terrestrial Photogrammetric Data Using Natural Surfaces as Control

We present a model for the alignment of image sets using topographic data as control information. Traditional photogrammetric practices are point-based and require well-measured and clearly marked entities within the scene; for repeated measurement, their demarcation should also be durable. Nonetheless, placement of photogrammetric control points oftentimes cannot be readily implemented within the area for documentation. In many of those cases, some form of topographic model, either coarse or fine, exists or can be readily established. This topographic description provides a broad and markerless coverage of control information, which, if modeled appropriately, can substitute the use of a control-point network in the alignment process. We present in this paper a registration model for terrestrial images based on surfaces as control information. The paper addresses both the problem of acquiring mutual corresponding entities and registration based on varying resolutions. The application of the proposed approach is demonstrated on a mapping of an archeological site and for registration of a set of terrestrial images of an open, unstructured scene using airborne laser scanning data. Results show both the applicability of such a model and that estimation comparable in quality to point based observations can be achieved.

Sauropod dinosaur research: a historical review

Abstract In the 169 years since Owen named a tooth as Cardiodon , the study of sauropod dinosaurs has gone through several distinct periods. In the early years, a sequence of descriptions of isolated skeletal elements gave rise to a gradually emerging understanding of the animals that would later be known as sauropods. The second phase began in 1871 with Phillips's description of Cetiosaurus oxoniensis , the first reasonably complete sauropod, and continued with the Marsh-Cope Bone Wars and the description of the nearly complete sauropods Camarasaurus and ‘ Brontosaurus ’ (= Apatosaurus ). As these and other genera became better known, a third phase began, exploring not just the remains but the lives of these giants, with arguments about posture and habitat to the fore, and with the public becoming increasingly aware of sauropods owing to skeletal mounts. A ‘dark age’ followed during and after World War II, with sauropods considered uninteresting evolutionary dead ends and largely ignored. This was brought to an end by the ‘dinosaur renaissance’ that began in the late 1960s, since when work has recommenced with new vigour, and the public has been introduced to a more vigorous and terrestrial image of sauropods through film and television. Both diversity and disparity of sauropods continue to increase through new descriptive work, and the group is now seen as more fascinating and worthy of study than ever before.

Generative statistical 3D reconstruction of unfoliaged trees from terrestrial images

This article presents a generative statistical approach for the automatic three-dimensional (3D) extraction and reconstruction of unfoliaged deciduous trees from terrestrial wide-baseline image sequences. Unfoliaged trees are difficult to reconstruct from images because of partially weak contrast, background clutter, occlusions, and particularly the possibly varying order of branches in images from different viewpoints. This work combines generative modeling by L-systems and a statistical approach for maximum a posteriori estimation for the reconstruction of the 3D branching structure of trees. Background estimation is conducted by means of gray scale morphology to provide a good basis for generative modeling. A Gaussian likelihood function based on intensity differences is used to evaluate the hypotheses. The target tree is classified into three typical branching types after the extraction of the first level of branches and specific production rules of an L-system are used. Generic prior distributions for parameters are refined based on already extracted branches in a Bayesian framework and are integrated into the maximum a posteriori estimation. By these means most of the branching structure besides the tiny twigs can be reconstructed. The results are presented in the form of virtual reality modeling language models and show the potential of the approach.

Orientation of Airborne Laser Scanning Point Clouds with Multi-View, Multi-Scale Image Blocks

Comprehensive 3D modeling of our environment requires integration of terrestrial and airborne data, which is collected, preferably, using laser scanning and photogrammetric methods. However, integration of these multi-source data requires accurate relative orientations. In this article, two methods for solving relative orientation problems are presented. The first method includes registration by minimizing the distances between of an airborne laser point cloud and a 3D model. The 3D model was derived from photogrammetric measurements and terrestrial laser scanning points. The first method was used as a reference and for validation. Having completed registration in the object space, the relative orientation between images and laser point cloud is known. The second method utilizes an interactive orientation method between a multi-scale image block and a laser point cloud. The multi-scale image block includes both aerial and terrestrial images. Experiments with the multi-scale image block revealed that the accuracy of a relative orientation increased when more images were included in the block. The orientations of the first and second methods were compared. The comparison showed that correct rotations were the most difficult to detect accurately by using the interactive method. Because the interactive method forces laser scanning data to fit with the images, inaccurate rotations cause corresponding shifts to image positions. However, in a test case, in which the orientation differences included only shifts, the interactive method could solve the relative orientation of an aerial image and airborne laser scanning data repeatedly within a couple of centimeters.

Monitoring lava-dome growth during the 2004–2008 Mount St. Helens, Washington, eruption using oblique terrestrial photography

We present an analysis of lava dome growth during the 2004–2008 eruption of Mount St. Helens using oblique terrestrial images from a network of remotely placed cameras. This underutilized monitoring tool augmented more traditional monitoring techniques, and was used to provide a robust assessment of the nature, pace, and state of the eruption and to quantify the kinematics of dome growth. Eruption monitoring using terrestrial photography began with a single camera deployed at the mouth of the volcano's crater during the first year of activity. Analysis of those images indicates that the average lineal extrusion rate decayed approximately logarithmically from about 8 m/d to about 2 m/d (± 2 m/d) from November 2004 through December 2005, and suggests that the extrusion rate fluctuated on time scales of days to weeks. From May 2006 through September 2007, imagery from multiple cameras deployed around the volcano allowed determination of 3-dimensional motion across the dome complex. Analysis of the multi-camera imagery shows spatially differential, but remarkably steady to gradually slowing, motion, from about 1–2 m/d from May through October 2006, to about 0.2–1.0 m/d from May through September 2007. In contrast to the fluctuations in lineal extrusion rate documented during the first year of eruption, dome motion from May 2006 through September 2007 was monotonic (± 0.10 m/d) to gradually slowing on time scales of weeks to months. The ability to measure spatial and temporal rates of motion of the effusing lava dome from oblique terrestrial photographs provided a significant, and sometimes the sole, means of identifying and quantifying dome growth during the eruption, and it demonstrates the utility of using frequent, long-term terrestrial photography to monitor and study volcanic eruptions.

Performance Analysis of the SIFT Operator for Automatic Feature Extraction and Matching in Photogrammetric Applications

In the photogrammetry field, interest in region detectors, which are widely used in Computer Vision, is quickly increasing due to the availability of new techniques. Images acquired by Mobile Mapping Technology, Oblique Photogrammetric Cameras or Unmanned Aerial Vehicles do not observe normal acquisition conditions. Feature extraction and matching techniques, which are traditionally used in photogrammetry, are usually inefficient for these applications as they are unable to provide reliable results under extreme geometrical conditions (convergent taking geometry, strong affine transformations, etc.) and for bad-textured images. A performance analysis of the SIFT technique in aerial and close-range photogrammetric applications is presented in this paper. The goal is to establish the suitability of the SIFT technique for automatic tie point extraction and approximate DSM (Digital Surface Model) generation. First, the performances of the SIFT operator have been compared with those provided by feature extraction and matching techniques used in photogrammetry. All these techniques have been implemented by the authors and validated on aerial and terrestrial images. Moreover, an auto-adaptive version of the SIFT operator has been developed, in order to improve the performances of the SIFT detector in relation to the texture of the images. The Auto-Adaptive SIFT operator (A2 SIFT) has been validated on several aerial images, with particular attention to large scale aerial images acquired using mini-UAV systems.