Hybrid Image-based Rendering for Free-view Synthesis

Image-based rendering (IBR) provides a rich toolset for free-viewpoint navigation in captured scenes. Many methods exist, usually with an emphasis either on image quality or rendering speed. In this paper we identify common IBR artifacts and combine the strengths of different algorithms to strike a good balance in the speed/quality tradeoff. First, we address the problem of visible color seams that arise from blending casually-captured input images by explicitly treating view-dependent effects. Second, we compensate for geometric reconstruction errors by refining per-view information using a novel clustering and filtering approach. Finally, we devise a practical hybrid IBR algorithm, which locally identifies and utilizes the rendering method best suited for an image region while retaining interactive rates. We compare our method against classical and modern (neural) approaches in indoor and outdoor scenes and demonstrate superiority in quality and/or speed.

Particle identification with the PANDA Barrel DIRC and the GlueX DIRC

Next-generation DIRC detectors, like the PANDA Barrel DIRC, with improved optical designs and better spatial and timing resolution, require correspondingly advanced reconstruction and PID methods. The investigation of the PID performance of two DIRC counters and the evaluation of the reconstruction and PID algorithms form the core of this thesis. Several reconstruction and PID approaches were developed, optimized, and tested using hadronic beam particles, experimental physics events, and Geant simulations. The near-final design of the PANDA Barrel DIRC was evaluated with a prototype in the T9 beamline at CERN in 2018. The analysis finds excellent agreement between the experimental data and the Geant simulations for all reconstruction algorithms. The best PID performance of up to $5.2 \pm 0.2$ s.d. $\pi$/K separation at 3.5 GeV/c, was obtained with a time imaging PID method. The PANDA Barrel DIRC simulation, as well as the reconstruction and PID algorithms, were evaluated using experimental data from the GlueX DIRC as part of the FAIR Phase-0 program. The performance validation was carried out using physics events of the GlueX experiment and simulations. The initial analysis results of the commissioning dataset show a $\pi$/K separation power of up to 3 s.d. at a momentum of 3.0-3.5 GeV/c, obtained using a geometric reconstruction algorithm.

ICE DETECTION ON AIRPLANE WINGS USING A PHOTOGRAMMETRIC POINT CLOUD: A SIMULATION

This study describes some tests carried out, within the European project (reference call: MANUNET III 2018, project code: MNET18/ICT-3438) called SEI (Spectral Evidence of ice), for the geometrical ice detection on airplane wings. The purpose of these analysis is to estimate thickness and shape of the ice that an RGB sensor is able to detect on large aircrafts as Boeing 737-800. However, field testing are not available yet, therefore, in order to simulate the final configuration, a steel panel has been used to reproduce the aircraft surface. The adopted methodology consists in defining a reference surface and modelling its 3D shape with and without ice through photogrammetric acquisitions collected by a DJI Mavic Air drone hosting a RGB camera and processed by Agisoft Metashape software. The comparison among models with and without the ice has been presented and results show that it is possible to identify the ice, even though some noise still remains due to the geometric reconstruction itself. Finally, using 3dReshaper and Matlab software, the authors develop various analysis defining the operative limits, the processing time, the correct setting up of Metashape for a more accurate ice detection, the optimization of the methodology in terms of processing time, precision and completeness. The procedure can certainly be more reliable considering the usage of the hyperspectral sensor technique as future implementation.

A Distributed Approach for the Implementation of Geometric Reconstruction-Based Visual SLAM Systems

SUMMARY Visual simultaneous localization and mapping (VSLAM) is a relevant solution for vehicle localization and mapping environments. However, it is computationally expensive because it demands large computational effort, making it a non-real-time solution. The VSLAM systems that employ geometric reconstructions are based on the parallel processing paradigm developed in the Parallel Tracking and Mapping (PTAM) algorithm. This type of system was created for processors that have exactly two cores. The various SLAM methods based on the PTAM were also not designed to scale to all the cores of modern processors nor to function as a distributed system. Therefore, we propose a modification to the pipeline for the execution of well-known VSLAM systems so that they can be scaled to all available processors during execution, thereby increasing their performance in terms of processing time. We explain the principles behind this modification via a study of the threads in the SLAM systems based on PTAM. We validate our results with experiments describing the behavior of the original ORB-SLAM system and the modified version.