Dr. Nayeli Shasni Schreyvogel Profile

Dr. Nayeli Shasni Schreyvogel

at Fraunhofer IOSB

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Publications (2)

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Proceedings Article | 2 October 2019 Presentation + Paper

Dense point cloud generation of urban scenes from nadir RGB images in a remote sensing system

Nayeli Schreyvogel, Jonas Mispelhorn, Wolfgang Middelmann

Proceedings Volume 11157, 111570K (2019) https://doi.org/10.1117/12.2531839

KEYWORDS: RGB color model, Cameras, LIDAR, Remote sensing, 3D image reconstruction

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A near real-time airborne 3D scanning system has been successfully implemented at Fraunhofer IOSB. This remote sensing system consists of a small aircraft and a ground control station. The aircraft is equipped with the following components: a Digital Acquisition System (DAQ), Inertial Navigation and Global Positioning Systems (INS/GPS), an Airborne Laser Scanner (ALS), and four industrial cameras. Two of these cameras (RGB and near-infrared ones) are nadir oriented, while the other two RGB cameras have an oblique orientation. The acquired LiDAR point clouds, images, and corresponding metadata are sent from the aircraft to the ground control station for further post-processing procedures, such as radiometric correction, boresight correction, and point cloud generation from images.

In this paper, the procedure regarding point cloud generation of urban scenes, with images from the nadir RGB camera, is described in detail. To produce dense point clouds three main steps are necessary: generation of disparity maps, creation of depth maps, and calculation of world coordinates (X, Y, and Z).

To create disparity maps, two adjacent images (stereopair) were rectified. Afterwards, the PatchMatch Stereo (PMS) algorithm for 3D reconstruction was executed, since it is easy to implement and provides good results according to the Middlebury Computer Vision dataset. Some steps were parallelized to optimize execution speed. Since depth is inversely proportional to disparity, depth maps were calculated from disparity maps. The height of scene elements Z was obtained by subtracting their depth to the camera height.

To calculate the remaining world coordinates X and Y, the back-projection equation and the camera intrinsic and extrinsic parameters were used. To validate the PMS algorithm, its resulting point cloud was compared with a LiDAR point cloud and a PhotoScan point cloud. The root mean square errors of both comparisons showed similar values.

Proceedings Article | 5 October 2017 Presentation + Paper

3D building reconstruction in a remote sensing workflow

Merlin Becker, Nayeli Espinosa, Wolfgang Middelmann, Irmgard Runkel

Proceedings Volume 10428, 1042807 (2017) https://doi.org/10.1117/12.2278855

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3D reconstruction and recognition of buildings and urban scenes is crucial for urban planning, disaster evaluation, historical, architectural and/or urban research and other photogrammetric tasks. The purpose of this research work is to evaluate 3D reconstruction of urban scenes by using the simplest workflow given by the optional IMAGINE UAV module for the ERDAS IMAGINE 2016 software. Digital elevation models and point clouds were extracted from RGB aerial images, which were captured by two SVS-Vistek nadir cameras with a resolution of 16 megapixel. The cameras were synchronized with a GPS/INS module for a direct georeferencing of the images. Additionally, the generated 3D models were imported to the Agiosoft PhotoScan application for a better visualization and to verify the focal length of the camera. The tridicon BuildingFinder software was used to improve the quality of the produced point clouds and for building recognition as well.

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