Combining visibility analysis and deep learning for refinement of semantic 3D building models by conflict classification

Semantic 3D building models are widely available and used in numerous applications. Such 3D building models display rich semantics but no façade openings, chiefly owing to their aerial acquisition techniques. Hence, refining models’ façades using dense, street-level, terrestrial point clouds seems a promising strategy. In this paper, we propose a method of combining visibility analysis and neural networks for enriching 3D models with window and door features. In the method, occupancy voxels are fused with classified point clouds, which provides semantics to voxels. Voxels are also used to identify conflicts between laser observations and 3D models. The semantic voxels and conflicts are combined in a Bayesian network to classify and delineate façade openings, which are reconstructed using a 3D model library. Unaffected building semantics is preserved while the updated one is added, thereby upgrading the building model to LoD3. Moreover, Bayesian network results are back-projected onto point clouds to improve points’ classification accuracy. We tested our method on a municipal CityGML LoD2 repository and the open point cloud datasets: TUM-MLS-2016 and TUM-FAÇADE. Validation results revealed that the method improves the accuracy of point cloud semantic segmentation and upgrades buildings with façade elements. The method can be applied to enhance the accuracy of urban simulations and facilitate the development of semantic segmentation algorithms

SU(2) solutions to self-duality equations in eight dimensions

We consider the octonionic self-duality equations on eight-dimensional manifolds of the form $M_8=M_4\times \R^4$, where $M_4$ is a hyper-K\"ahler four-manifold. We construct explicit solutions to these equations and their symmetry reductions to the non-abelian Seiberg-Witten equations on $M_4$ in the case when the gauge group is SU(2). These solutions are singular for flat and Eguchi-Hanson backgrounds. For $M_4=\R\times {\mathcal G}$ with a cohomogeneity one hyper-K\"ahler metric, where ${\mathcal G}$ is a nilpotent (Bianchi II) Lie group, we find a solution which is singular only on a single-sided domain wall. This gives rise to a regular solution of the non-abelian Seiberg-Witten equations on a four-dimensional nilpotent Lie group which carries a regular conformally hyper-K\"ahler metric.Comment: Dedicated to Jerzy Lukierski on the occasion of his 75th birthday. Final version, to appear in JG

Theoretical Study: High Harmonic Generation by Light Transients

The dynamic of electron densities in matter upon the interaction with an intense, few-cycle electric field of light causes variety of nonlinear phenomena. Capturing the spatiotemporal dynamics of electrons calls for isolated attosecond pulses in the X-ray regime, with sufficient flux to allow for: (i) attosecond pump-attosecond probe spectroscopy;or (ii) four-dimensional imaging. Light field synthesizers generate arbitrary sub-cycle, non-sinusoidal waveforms. They have a great potential to overcome the limitations of current laser sources and to extend attosecond pulses towards the X-ray regime. In this paper, we show theoretically how the achievable high-energy, high-power waveforms from current light field synthesizers can be optimized to enhance the harmonic yield at high photon energies and can serve as a promising source for scaling the photon energies of attosecond pulses. We demonstrate that the simulated optimized, non-sinusoidal waveform in this work can increase the photon flux of keV, attosecond pulses by five orders of magnitude compared to the achievable flux from longer wavelength sources and at similar photon energies

Supervised detection of bomb craters in historical aerial images using convolutional neural networks

The aftermath of the air strikes during World War II is still present today. Numerous bombs dropped by planes did not explode, still exist in the ground and pose a considerable explosion hazard. Tracking down these duds can be tackled by detecting bomb craters. The existence of a dud can be inferred from the existence of a crater. This work proposes a method for the automatic detection of bomb craters in aerial wartime images. First of all, crater candidates are extracted from an image using a blob detector. Based on given crater references, for every candidate it is checked whether it, in fact, represents a crater or not. Candidates from various aerial images are used to train, validate and test Convolutional Neural Networks (CNNs) in the context of a two-class classification problem. A loss function (controlling what the CNNs are learning) is adapted to the given task. The trained CNNs are then used for the classification of crater candidates. Our work focuses on the classification of crater candidates and we investigate if combining data from related domains is beneficial for the classification. We achieve a F1-score of up to 65.4% when classifying crater candidates with a realistic class distribution. © Authors 2019. CC BY 4.0 License

REGISTRATION OF UAV DATA AND ALS DATA USING POINT TO DEM DISTANCES FOR BATHYMETRIC CHANGE DETECTION

This paper shows a method to register point clouds from images of UAV-mounted airborne cameras as well as airborne laser scanner data. The focus is a general technique which does rely neither on linear or planar structures nor on the point cloud density. Therefore, the proposed approach is also suitable for rural areas and water bodies captured via different sensor configurations. This approach is based on a regular 2.5D grid generated from the segmented ground points of the 3D point cloud. It is assumed that initial values for the registration are already estimated, e.g. by measured exterior orientation parameters with the UAV mounted GNSS and IMU. These initial parameters are finely tuned by minimizing the distances between the 3D points of a target point cloud to the generated grid of the source point cloud in an iteration process. To eliminate outliers (e.g., vegetation points) a threshold for the distances is defined dynamically at each iteration step, which filters ground points during the registration. The achieved accuracy of the registration is up to 0.4 m in translation and up to 0.3 degrees in rotation, by using a raster size of the DEM of 2 m. Considering the ground sampling distance of the airborne data which is up to 0.4 m between the scan lines, this result is comparable to the result achieved by an ICP algorithm, but the proposed approach does not rely on point densities and is therefore able to solve registrations where the ICP have difficulties

EXTRACTION OF SOLAR CELLS FROM UAV-BASED THERMAL IMAGE SEQUENCES

This paper discusses the automatic generation of thermal infrared ortho image mosaics and the extraction of solar cells from these ortho image mosaics. Image sequences are recorded by a thermal infrared (TIR) camera mounted on a remotely piloted aerial system (RPAS). The image block is relatively oriented doing a bundle block adjustment and transferred to a local coordinate system using ground control points. The resulting ortho image mosaic is searched for solar cells. A library of templates of solar cells from thermal images is used to learn an implicit shape model. The extraction of the single solar cells is done by estimating corners and centre points of cells using these shape models in a Markov-Chain-Monte-Carlo algorithm by combining four corners and a centre point. As for the limited geometric resolution and radiometric contrast, most of the cells are not directly detected. An iterative process based on the knowledge of the regular grid structure of a solar cell installation is used to predict further cells and verify their existence by repeating the corner extraction and grammar combination. Results show that this work flow is able to detect most of the solar cells under the condition that the cells have a more or less common radiometric behaviour and no reflections i.e. from the sun occur. The cells need a rectangular shape and have the same orientation so that the model of the grammar is applicable to the solar cells

FUSION OF 3D POINT CLOUDS WITH TIR IMAGES FOR INDOOR SCENE RECONSTRUCTION

Obtaining accurate 3D descriptions in the thermal infrared (TIR) is a quite challenging task due to the low geometric resolutions of TIR cameras and the low number of strong features in TIR images. Combining the radiometric information of the thermal infrared with 3D data from another sensor is able to overcome most of the limitations in the 3D geometric accuracy. In case of dynamic scenes with moving objects or a moving sensor system, a combination with RGB cameras and profile laserscanners is suitable. As a laserscanner is an active sensor in the visible red or near infrared (NIR) and the thermal infrared camera captures the radiation emitted by the objects in the observed scene, the combination of these two sensors for close range applications are independent from external illumination or textures in the scene. This contribution focusses on the fusion of point clouds from terrestrial laserscanners and RGB cameras with images from thermal infrared mounted together on a robot for indoor 3D reconstruction. The system is geometrical calibrated including the lever arm between the different sensors. As the field of view is different for the sensors, the different sensors record the same scene points not exactly at the same time. Thus, the 3D scene points of the laserscanner and the photogrammetric point cloud from the RGB camera have to be synchronized before point cloud fusion and adding the thermal channel to the 3D points

Tailoring the transverse mode of a high-finesse optical resonator with stepped mirrors

Enhancement cavities (ECs) seeded with femtosecond pulses have developed into the most powerful technique for high-order harmonic generation (HHG) at repetition rates in the tens of MHz. Here, we demonstrate the feasibility of controlling the phase front of the excited transverse eigenmode of a ring EC by using mirrors with stepped surface profiles, while maintaining the high finesse required to reach the peak intensities necessary for HHG. The two lobes of a TEM01 mode of a 3.93m long EC, seeded with a single-frequency laser, are delayed by 15.6 fs with respect to each other before a tight focus, and the delay is reversed after the focus. The tailored transverse mode exhibits an on-axis intensity maximum in the focus. Furthermore, the geometry is designed to generate a rotating wavefront in the focus when few-cycle pulses circulate in the EC. This paves the way to gating isolated attosecond pulses (IAPs) in a transverse manner (similarly to the attosecond lighthouse), heralding IAPs at repetition rates well into the multi-10MHz range. In addition, these results promise high-efficiency harmonic output coupling from ECs in general, with an unparalleled power scalability. These prospects are expected to tremendously benefit photoelectron spectroscopy and extreme-ultraviolet frequency comb spectroscopy