Dense Point Cloud Extraction From Oblique Imagery

With the increasing availability of low-cost digital cameras with small or medium sized sensors, more and more airborne images are available with high resolution, which enhances the possibility in establishing three dimensional models for urban areas. The high accuracy of representation of buildings in urban areas is required for asset valuation or disaster recovery. Many automatic methods for modeling and reconstruction are applied to aerial images together with Light Detection and Ranging (LiDAR) data. If LiDAR data are not provided, manual steps must be applied, which results in semi-automated technique. The automated extraction of 3D urban models can be aided by the automatic extraction of dense point clouds. The more dense the point clouds, the easier the modeling and the higher the accuracy. Also oblique aerial imagery provides more facade information than nadir images, such as building height and texture. So a method for automatic dense point cloud extraction from oblique images is desired. In this thesis, a modified workflow for the automated extraction of dense point clouds from oblique images is proposed and tested. The result reveals that this modified workflow works well and a very dense point cloud can be extracted from only two oblique images with slightly higher accuracy in flat areas than the one extracted by the original workflow. The original workflow was established by previous research at the Rochester Institute of Technology (RIT) for point cloud extraction from nadir images. For oblique images, a first modification is proposed in the feature detection part by replacing the Scale-Invariant Feature Transform (SIFT) algorithm with the Affine Scale-Invariant Feature Transform (ASIFT) algorithm. After that, in order to realize a very dense point cloud, the Semi-Global Matching (SGM) algorithm is implemented in the second modification to compute the disparity map from a stereo image pair, which can then be used to reproject pixels back to a point cloud. A noise removal step is added in the third modification. The point cloud from the modified workflow is much denser compared to the result from the original workflow. An accuracy assessment is made in the end to evaluate the point cloud extracted from the modified workflow. From the two flat areas, subsets of points are selected from both original and modified workflow, and then planes are fitted to them, respectively. The Mean Squared Error (MSE) of the points to the fitted plane is compared. The point subsets from the modified workflow have slightly lower MSEs than the ones from the original workflow, respectively. This suggests a much more dense and more accurate point cloud can lead to clear roof borders for roof extraction and improve the possibility of 3D feature detection for 3D point cloud registration

Deep Cross-Domain Building Extraction for Selective Depth Estimation from Oblique Aerial Imagery

With the technological advancements of aerial imagery and accurate 3d reconstruction of urban environments, more and more attention has been paid to the automated analyses of urban areas. In our work, we examine two important aspects that allow online analysis of building structures in city models given oblique aerial image sequences, namely automatic building extraction with convolutional neural networks (CNNs) and selective real-time depth estimation from aerial imagery. We use transfer learning to train the Faster R-CNN method for real-time deep object detection, by combining a large ground-based dataset for urban scene understanding with a smaller number of images from an aerial dataset. We achieve an average precision (AP) of about 80% for the task of building extraction on a selected evaluation dataset. Our evaluation focuses on both dataset-specific learning and transfer learning. Furthermore, we present an algorithm that allows for multi-view depth estimation from aerial image sequences in real-time. We adopt the semi-global matching (SGM) optimization strategy to preserve sharp edges at object boundaries. In combination with the Faster R-CNN, it allows a selective reconstruction of buildings, identified with regions of interest (RoIs), from oblique aerial imagery

DEEP CROSS-DOMAIN BUILDING EXTRACTION FOR SELECTIVE DEPTH ESTIMATION FROM OBLIQUE AERIAL IMAGERY

With the technological advancements of aerial imagery and accurate 3d reconstruction of urban environments, more and more attention has been paid to the automated analyses of urban areas. In our work, we examine two important aspects that allow online analysis of building structures in city models given oblique aerial image sequences, namely automatic building extraction with convolutional neural networks (CNNs) and selective real-time depth estimation from aerial imagery. We use transfer learning to train the Faster R-CNN method for real-time deep object detection, by combining a large ground-based dataset for urban scene understanding with a smaller number of images from an aerial dataset. We achieve an average precision (AP) of about 80 % for the task of building extraction on a selected evaluation dataset. Our evaluation focuses on both dataset-specific learning and transfer learning. Furthermore, we present an algorithm that allows for multi-view depth estimation from aerial image sequences in real-time. We adopt the semi-global matching (SGM) optimization strategy to preserve sharp edges at object boundaries. In combination with the Faster R-CNN, it allows a selective reconstruction of buildings, identified with regions of interest (RoIs), from oblique aerial imagery

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

OBLIQUE MULTI-CAMERA SYSTEMS - ORIENTATION AND DENSE MATCHING ISSUES

International audience3D Optical Metrology (3DOM) unit, Bruno Kessler Foundation (FBK), Trento, Italy <rupnik, franex, remondino>@fbk.eu, http://3dom.fbk.eu Commission III-WG4 ABS TRACT: The use of oblique imagery has become a standard for many civil and mapping applications, thanks to the development of airborne digital multi-camera systems, as proposed by many companies (Blomoblique, IGI, Leica, M idas, Pictometry, Vexcel/M icrosoft, VisionM ap, etc.). The indisputable virtue of oblique photography lies in its simplicity of interpretation and understanding for inexperienced users allowing their use of oblique images in very different applications, such as building detection and reconstruction, building structural damage classification, road land updating and administration services, etc. The paper reports an overview of the actual oblique commercial systems and presents a workflow for the automated orientation and dense matching of large image blocks. Perspectives, potentialities, pitfalls and suggestions for achieving satisfactory results are given. Tests performed on two datasets acquired with two multi-camera systems over urban areas are also reported. Figure 1: Large urban area pictured with an oblique multi-camera system. Once advanced image triangulation methods have retrieved interior and exterior parameters of the cameras, dense point clouds can be deriv ed for 3D city modelling, feature extraction and mapping purposes

OBLIQUE PHOTOGRAMMETRY SUPPORTING 3D URBAN RECONSTRUCTION OF COMPLEX SCENARIOS

open9siAccurate 3D city models represent an important source of geospatial information to support various “smart city” applications, such as space management, energy assessment, 3D cartography, noise and pollution mapping as well as disaster management. Even though remarkable progress has been made in recent years, there are still many open issues, especially when it comes to the 3D modelling of complex urban scenarios like historical and densely-built city centres featuring narrow streets and non-conventional building shapes. Most approaches introduce strong building priors/constraints on symmetry and roof typology that penalize urban environments having high variations of roof shapes. Furthermore, although oblique photogrammetry is rapidly maturing, the use of slanted views for façade reconstruction is not completely included in the reconstruction pipeline of state-of-the-art software. This paper aims to investigate state-of-the-art methods for 3D building modelling in complex urban scenarios with the support of oblique airborne images. A reconstruction approach based on roof primitives fitting is tested. Oblique imagery is then exploited to support the manual editing of the generated building models. At the same time, mobile mapping data are collected at cm resolution and then integrated with the aerial ones. All approaches are tested on the historical city centre of Bergamo (Italy).openToschi, I.; Ramos, M. M.; Nocerino, E.; Menna, F.; Remondino, F.; Moe, K.; Poli, D.; Legat, K.; Fassi, F.Toschi, I.; Ramos, M. M.; Nocerino, E.; Menna, F.; Remondino, F.; Moe, K.; Poli, D.; Legat, K.; Fassi, Francesc

OBLIQUE PHOTOGRAMMETRY SUPPORTING 3D URBAN RECONSTRUCTION OF COMPLEX SCENARIOS

Accurate 3D city models represent an important source of geospatial information to support various “smart city” applications, such as space management, energy assessment, 3D cartography, noise and pollution mapping as well as disaster management. Even though remarkable progress has been made in recent years, there are still many open issues, especially when it comes to the 3D modelling of complex urban scenarios like historical and densely-built city centres featuring narrow streets and non-conventional building shapes. Most approaches introduce strong building priors/constraints on symmetry and roof typology that penalize urban environments having high variations of roof shapes. Furthermore, although oblique photogrammetry is rapidly maturing, the use of slanted views for façade reconstruction is not completely included in the reconstruction pipeline of state-of-the-art software. This paper aims to investigate state-of-the-art methods for 3D building modelling in complex urban scenarios with the support of oblique airborne images. A reconstruction approach based on roof primitives fitting is tested. Oblique imagery is then exploited to support the manual editing of the generated building models. At the same time, mobile mapping data are collected at cm resolution and then integrated with the aerial ones. All approaches are tested on the historical city centre of Bergamo (Italy)