Extraction of three-dimensional structure of a scene from stereo images is a problem that has been studied by the computer vision community for decades. Early work focused on the fundamentals of image correspondence and stereo geometry. Stereo research has matured significantly throughout the years and many advances in computational stereo continue to be made, allowing stereo to be applied to new and more demanding problems. We review recent advances in computational stereo, focusing primarily on three important topics: correspondence methods, methods for occlusion, and real-time implementations. Throughout, we present tables that summarize and draw distinctions among key ideas and approaches. Where available, we provide comparative analyses and we make suggestions for analyses yet to be done.

https://www.cs.jhu.edu/~burschka/publications/StereoPAMI.pdf

Advances in computational stereo

The extraction of curvilinear structures is an important low-level operation in computer vision that has many applications. Most existing operators use a simple model for the line that is to be extracted, i.e., they do not take into account the surroundings of a line. This leads to the undesired consequence that the line will be extracted in the wrong position whenever a line with different lateral contrast is extracted. In contrast, the algorithm proposed in this paper uses an explicit model for lines and their surroundings. By analyzing the scale-space behavior of a model line profile, it is shown how the bias that is induced by asymmetrical lines can be removed. Furthermore, the algorithm not only returns the precise subpixel line position, but also the width of the line for each line point, also with subpixel accuracy.

/pdf/an-unbiased-detector-of-curvilinear-structures-clitho9vlv.pdf

An unbiased detector of curvilinear structures

Remote sensing of impervious surfaces in the urban areas: Requirements, methods, and trends

A comparison between data acquisition and processing from passive optical sensors and airborne laser scanning is presented. A short overview and the major differences between the two technologies are outlined. Advantages and disadvantages with respect to various aspects are discussed, like sensors, platforms, flight planning, data acquisition conditions, imaging, object reflectance, automation, accuracy, flexibility and maturity, production time and costs. A more detailed comparison is presented with respect to DTM and DSM generation. Strengths of laser scanning with respect to certain applications are outlined. Although airborne laser scanning competes to a certain extent with photogrammetry and will replace it in certain cases, the two technologies are fairly complementary and their integration can lead to more accurate and complete products, and open up new areas of application.

http://www.lidar.com.br/arquivos/comparisonlaserphotog.pdf

A comparison between photogrammetry and laser scanning

Maps depict natural and human-induced changes on earth at a fine resolution for large areas and over long periods of time. In addition, maps—especially historical maps—are often the only information source about the earth as surveyed using geodetic techniques. In order to preserve these unique documents, increasing numbers of digital map archives have been established, driven by advances in software and hardware technologies. Since the early 1980s, researchers from a variety of disciplines, including computer science and geography, have been working on computational methods for the extraction and recognition of geographic features from archived images of maps (digital map processing). The typical result from map processing is geographic information that can be used in spatial and spatiotemporal analyses in a Geographic Information System environment, which benefits numerous research fields in the spatial, social, environmental, and health sciences. However, map processing literature is spread across a broad range of disciplines in which maps are included as a special type of image. This article presents an overview of existing map processing techniques, with the goal of bringing together the past and current research efforts in this interdisciplinary field, to characterize the advances that have been made, and to identify future research directions and opportunities.

A Survey of Digital Map Processing Techniques

General Topics and Scene Reconstruction- An Overview of DARPA's Research Program in Automatic Population of Geospatial Databases- A Testbed for the Evaluation of Feature Extraction Techniques in a Time Constrained Environment- The Role of Artificial Intelligence in the Reconstruction of Man-Made Objects from Aerial Images- Scene Reconstruction Research - Towards an Automatic System- Semantic Modelling of Man-Made Objects by Production Nets- From Large-Scale DTM Extraction to Feature Extraction- Building Detection and Reconstruction- 3-D Building Reconstruction with ARUBA: A Qualitative and Quantitative Evaluation- A System for Building Detection from Aerial Images- On the Reconstruction of Urban House Roofs from Aerial Images- Image-Based Reconstruction of Informal Settlements- A Model Driven Approach to Extract Buildings from Multi-View Aerial Imagery- Automated Building Extraction from Digital Stereo Imagery- Application of Semi-Automatic Building Acquisition- On the Integration of Object Modeling and Image Modeling in Automated Building Extraction from Aerial Images- TOBAGO - A Topology Builder for the Automated Generation of Building Models- Crestlines Constribution to the Automatic Building Extraction- Recognizing Buildings in Aerial Image- Above-Ground Objects in Urban Scenes from Medium Scale Aerial Imagery- Digital Surface Models for Building Extraction- Extracting Artificial Surface Objects from Airborne Laser Scanner Data- Interpretation of Urban Surface Models using 2D Building Information- Least Squares Matching for Three Dimensional Building Reconstruction- Assessment of the Effects of Resolution on Automated DEM and Building Extraction- Road Extraction- The Role of Grouping for Road Extraction- Artificial Intelligence in 3-D Feature Extraction- Updating Road Maps by Contextual Reasoning- Fast Robust Tracking of Curvy Partially Occluded Roads in Clutter in Aerial Images- Linear Feature Extraction with 3-D LSB-Snakes- Context-Supported Road Extraction- Map/GIS-Based Methods- Three-Dimensional Description of Dense Urban Areas using Maps and Aerial Images- MOSES: A Structural Approach to Aerial Image Understanding- An Approach for the Extraction of Settlement Areas- Extraction of Polygonal Features from Satellite Images for Automatic Registration: The ARCHANGEL Project- Visualisation- A Set of Visualization Data Needs in Urban Environmental Planning & Design for Photogrammetric Data- A Virtual Reality Model of a Major International Airport- Managing Large 3D Urban Database Contents Supporting Phototexture and Levels of Detail- List of Workshop Participants- Author Index

/pdf/automatic-extraction-of-man-made-objects-from-aerial-and-5bbvmwlxid.pdf

Automatic Extraction of Man-Made Objects from Aerial and Space Images (II)

We present a technique to extract complex suburban roofs from sets of aerial images. Because we combine 2-D edge information, photometric and chromatic attributes and 3-D information, we can deal with complex houses. Neither do we assume the roofs to be flat or rectilinear nor do we require parameterized building models. From only one image, 2-D edges and their corresponding attributes and relations are extracted. Using a segment stereo matching based on all available images, the 3-D location of these edges are computed. The 3-D segments are then grouped into planes and 2-D enclosures are extracted, thereby allowing to infer adjoining 3-D patches describing roofs of houses. To achieve this, we have developed a hierarchical procedure that effectively pools the information while keeping the combinatorics under control. Of particular importance is the tight coupling of 2-D and 3-D analysis.

/pdf/automatic-extraction-of-generic-house-roofs-from-high-3p0wq6pbjv.pdf

Automatic Extraction of Generic House Roofs from High Resolution Aerial Imagery

Reliable and accurate 3-D reconstruction of man-made objects is essential for many applications using digital 3-D city models. Manual reconstruction of buildings from aerial images is time consuming and requires skilled personnel, hence large efforts are being directed towards the automation of building detection and reconstruction. In this paper we present ARUBA — a framework for automated 3-D building reconstruction. After highlighting our strategy and concisely describing the framework and its modules, we evaluate the reconstructed roofs relative to accurate reference data based on three criteria: completeness, geometric accuracy and shape similarity. Finally, we interpret the results of the performance evaluation and make suggestions for improvements.

/pdf/3-d-building-reconstruction-with-aruba-a-qualitative-and-5811y2u6pf.pdf

3-D Building Reconstruction with ARUBA: A Qualitative and Quantitative Evaluation

The Role of Color Attributes and Similarity Grouping in 3-D Building Reconstruction

Automation of Digital Terrain Model Generation and Man-Made Object Extraction from Aerial Images (AMOBE) is a joint project between the Institute of Geodesy and Photogrammetry (IGP) and the Institute of Communications Technology (Image Science Group) (IKT) at the Swiss Federal Institute of Technology in Zurich. In the project we develop methods and algorithms to detect and reconstruct man-made objects, such as buildings and roads, and to generate Digital Surface Models (DSMs) from high resolution aerial images. Primary attention in AMOBE focuses on high quality reconstruction of buildings as being one of the more predominantly and frequently occurring 3-D man-made objects in high-resolution aerial imagery. In this paper we present our research strategy, current results, and make an outlook onto future work.

Project AMOBE, Automation of digital terrain model generation and Mad-made OBject Extraction from aerial images: strategies, current status and future work

Olof Henricsson

Papers

Automatic Extraction of Man-Made Objects from Aerial and Space Images (II)

Automatic Extraction of Generic House Roofs from High Resolution Aerial Imagery

3-D Building Reconstruction with ARUBA: A Qualitative and Quantitative Evaluation

The Role of Color Attributes and Similarity Grouping in 3-D Building Reconstruction

Project AMOBE, Automation of digital terrain model generation and Mad-made OBject Extraction from aerial images: strategies, current status and future work