455 research outputs found
New instruments and technologies for Cultural Heritage survey: full integration between point clouds and digital photogrammetry
In the last years the Geomatic Research Group of the Politecnico di Torino faced some new research topics about new instruments for point cloud generation (e.g. Time of Flight cameras) and strong integration between multi-image matching techniques and 3D Point Cloud information in order to solve the ambiguities of the already known matching algorithms. ToF cameras can be a good low cost alternative to LiDAR instruments for the generation of precise and accurate point clouds: up to now the application range is still limited but in a near future they will be able to satisfy the most part of the Cultural Heritage metric survey requirements. On the other hand multi-image matching techniques with a correct and deep integration of the point cloud information can give the correct solution for an "intelligent" survey of the geometric object break-lines, which are the correct starting point for a complete survey. These two research topics are strictly connected to a modern Cultural Heritage 3D survey approach. In this paper after a short analysis of the achieved results, an alternative possible scenario for the development of the metric survey approach inside the wider topic of Cultural Heritage Documentation is reporte
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
Self-supervised monocular depth estimation from oblique UAV videos
UAVs have become an essential photogrammetric measurement as they are
affordable, easily accessible and versatile. Aerial images captured from UAVs
have applications in small and large scale texture mapping, 3D modelling,
object detection tasks, DTM and DSM generation etc. Photogrammetric techniques
are routinely used for 3D reconstruction from UAV images where multiple images
of the same scene are acquired. Developments in computer vision and deep
learning techniques have made Single Image Depth Estimation (SIDE) a field of
intense research. Using SIDE techniques on UAV images can overcome the need for
multiple images for 3D reconstruction. This paper aims to estimate depth from a
single UAV aerial image using deep learning. We follow a self-supervised
learning approach, Self-Supervised Monocular Depth Estimation (SMDE), which
does not need ground truth depth or any extra information other than images for
learning to estimate depth. Monocular video frames are used for training the
deep learning model which learns depth and pose information jointly through two
different networks, one each for depth and pose. The predicted depth and pose
are used to reconstruct one image from the viewpoint of another image utilising
the temporal information from videos. We propose a novel architecture with two
2D CNN encoders and a 3D CNN decoder for extracting information from
consecutive temporal frames. A contrastive loss term is introduced for
improving the quality of image generation. Our experiments are carried out on
the public UAVid video dataset. The experimental results demonstrate that our
model outperforms the state-of-the-art methods in estimating the depths.Comment: Submitted to ISPRS Journal of Photogrammetry and Remote Sensin
Integration of range and image data for building reconstruction
The extraction of information from image and range data is one of the main research topics. In literature, several papers dealing with this topic has been already presented. In particular, several authors have suggested an integrated use of both range and image information in order to increase the reliability and the completeness of the results exploiting their complementary nature. In this paper, an integration between range and image data for the geometric reconstruction of man-made object is presented. The focus is on the edge extraction procedure performed in an integrated way exploiting both the from range and image data. Both terrestrial and aerial applications have been analysed for the faade extraction in terrestrial acquisitions and the roof outline extraction from aerial data. The algorithm and the achieved results will be described and discussed in detail
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