Data Fusion of Objects Using Techniques Such as Laser Scanning, Structured Light and Photogrammetry for Cultural Heritage Applications

In this paper we present a semi-automatic 2D-3D local registration pipeline capable of coloring 3D models obtained from 3D scanners by using uncalibrated images. The proposed pipeline exploits the Structure from Motion (SfM) technique in order to reconstruct a sparse representation of the 3D object and obtain the camera parameters from image feature matches. We then coarsely register the reconstructed 3D model to the scanned one through the Scale Iterative Closest Point (SICP) algorithm. SICP provides the global scale, rotation and translation parameters, using minimal manual user intervention. In the final processing stage, a local registration refinement algorithm optimizes the color projection of the aligned photos on the 3D object removing the blurring/ghosting artefacts introduced due to small inaccuracies during the registration. The proposed pipeline is capable of handling real world cases with a range of characteristics from objects with low level geometric features to complex ones

A global streamflow reanalysis for 1980–2018

Global and continental scale hydrological reanalysis datasets receive growing attention due to their increasing number of applications, ranging from water resources management, climate change studies, water related hazards and policy support. Until recently, their use was mostly limited to qualitative assessments, due to their coarse spatial and temporal resolution, large uncertainty and bias in the model output, and limited extent of the dataset in space and time. This research reports on the setup of a gridded hydrological model with quasi-global coverage, able to reproduce a seamless 39-year streamflow simulation in all world’s medium to large river basins. The model was calibrated at 1226 river sections with a total drainage area of 51 million km2 within 66 countries, using ECMWF’s latest atmospheric reanalysis ERA5. A performance assessment revealed large improvements in reproducing past discharge observations, in comparison to the calibration used in the current operational setup of the hydrological model as part of the Copernicus – Global Flood Awareness System (GloFAS, www.globalfloods.eu), with median scores of Kling-Gupta Efficiency KGE = 0.67 and correlation r = 0.8. The simulation bias was also dramatically reduced and narrowed around zero, with more than 60% of stations showing percent bias within ±20%. Pronounced regional differences in the simulation results remain, pointing out the need for detailed investigation of the hydrological processes in specific regions, including parts of Africa and South Asia. In addition, observed discharges with high data quality is key to achieving skillful model output. The new calibrated model will become part of the operational runs of GloFAS in the next system release foreseen for Spring 2020, together with a near real time extension of the streamflow reanalysis

Evaluation of ERA5 and WFDE5 forcing data for hydrological modelling and the impact of bias correction with regional climatologies: A case study in the Danube River Basin

Study region: The Danube River Basin. Study focus: Hydrological modelling of large, heterogeneous watersheds requires appropriate meteorological forcing data. The global meteorological reanalysis ERA5 and the global forcing dataset WFDE5 were evaluated for driving an uncalibrated setup of the mechanistic hydrological model PROMET (0.00833333 degrees/1 h resolution) for the period 1980-2016. Different climatologies were used for linear bias correction of ERA5: the global WorldClim 2 temperature and precipitation climatologies and the regional GLOWA and PRISM Alpine precipitation climatologies. Simulations driven with the uncorrected ERA5 reanalysis, the WFDE5 forcing dataset, ERA5 biascorrected with WorldClim 2 and ERA5 bias-corrected with a GLOWA-PRISM-WorldClim 2 mosaic were evaluated regarding percent bias of discharge and model efficiency. New hydrological insights for the region: Simulations yielded good model efficiencies and low percent biases of discharge at selected gauges. Uncalibrated model efficiencies corresponded with previous hydrological modelling studies. ERA5 and WFDE5 were well suited to drive PROMET in the hydrologically complex Danube basin, but bias correction of precipitation was essential for ERA5. The ERA5-driven simulation bias-corrected with a GLOWA-PRISM-WorldClim 2 mosaic performed best. Bias correction with GLOWA and PRISM outperformed WorldClim 2 in the Alps due to more realistic small-scale Alpine precipitation patterns resulting from higher station densities. In mountainous terrain, we emphasize the need for regional high-resolution precipitation climatologies and recommend them for bias correction of precipitation rather than global datasets

Automatic face recognition using stereo images

Face recognition is an important pattern recognition problem, in the study of both natural and artificial learning problems. Compaxed to other biometrics, it is non-intrusive, non- invasive and requires no paxticipation from the subjects. As a result, it has many applications varying from human-computer-interaction to access control and law-enforcement to crowd surveillance. In typical optical image based face recognition systems, the systematic vaxiability arising from representing the three-dimensional (3D) shape of a face by a two-dimensional (21)) illumination intensity matrix is treated as random vaxiability. Multiple examples of the face displaying vaxying pose and expressions axe captured in different imaging conditions. The imaging environment, pose and expressions are strictly controlled and the images undergo rigorous normalisation and pre-processing. This may be implemented in a paxtially or a fully automated system. Although these systems report high classification accuracies (>90%), they lack versatility and tend to fail when deployed outside laboratory conditions. Recently, more sophisticated 3D face recognition systems haxnessing the depth information have emerged. These systems usually employ specialist equipment such as laser scanners and structured light projectors. Although more accurate than 2D optical image based recognition, these systems are equally difficult to implement in a non-co-operative environment. Existing face recognition systems, both 2D and 3D, detract from the main advantages of face recognition and fail to fully exploit its non-intrusive capacity. This is either because they rely too much on subject co-operation, which is not always available, or because they cannot cope with noisy data. The main objective of this work was to investigate the role of depth information in face recognition in a noisy environment. A stereo-based system, inspired by the human binocular vision, was devised using a pair of manually calibrated digital off-the-shelf cameras in a stereo setup to compute depth information. Depth values extracted from 2D intensity images using stereoscopy are extremely noisy, and as a result this approach for face recognition is rare. This was cofirmed by the results of our experimental work. Noise in the set of correspondences, camera calibration and triangulation led to inaccurate depth reconstruction, which in turn led to poor classifier accuracy for both 3D surface matching and 211) 2 depth maps. Recognition experiments axe performed on the Sheffield Dataset, consisting 692 images of 22 individuals with varying pose, illumination and expressions

Investigation of Pyrolysis Gas Chemistry in an Inductively Coupled Plasma Facility

The pyrolysis mechanics of Phenolic Impregnated Carbon Ablators (PICA) makes it a valued material for use in thermal protection systems for spacecraft atmospheric re-entry. The present study of the interaction of pyrolysis gases and char with plasma gases in the boundary layer over PICA and its substrate, FiberForm, extends previous work on this topic that has been done in the UVM 30 kW Inductively Coupled Plasma (ICP) Torch Facility. Exposure of these material samples separately to argon, nitrogen, oxygen, air, and carbon dioxide plasmas, and combinations of said test gases provides insight into the evolution of the pyrolysis gases as they react with the different environments. Measurements done to date include time-resolved absolute emission spectroscopy, location-based temperature response, flow characterization of temperature, enthalpy, and enthalpy flux, and more recently, spatially resolved and high-resolution emission spectroscopy, all of which provide measure of the characteristics of the pyrolysis chemistry and material response. Flow characterization tests construct an general knowledge of the test condition temperature, composition, and enthalpy. Tests with relatively inert argon plasmas established a baseline for the pyrolysis gases that leave the material. Key pyrolysis species such as CN Violet bands, NH, OH and Hydrogen Alpha (Hα) lines were seen with relative repeatability in temporal, spectral, and intensity values. Tests with incremental addition, and static mixtures, of reactive plasmas provided a preliminary image of how the gases interacted with atmospheric flows and other pyrolysis gases. Evidence of a temporal relationship between NH and Hα relating to nitrogen addition is seen, as well as a similar relationship between OH and Hα in oxygen based environments. Temperature analysis highlighted the reaction of the material to various flow conditions and displayed the in depth material response to argon and air/argon plasmas. The development of spatial emission analysis has been started with the hope of better resolving the previously seen pyrolysis behavior in time and space

Scalable, Detailed and Mask-Free Universal Photometric Stereo

In this paper, we introduce SDM-UniPS, a groundbreaking Scalable, Detailed, Mask-free, and Universal Photometric Stereo network. Our approach can recover astonishingly intricate surface normal maps, rivaling the quality of 3D scanners, even when images are captured under unknown, spatially-varying lighting conditions in uncontrolled environments. We have extended previous universal photometric stereo networks to extract spatial-light features, utilizing all available information in high-resolution input images and accounting for non-local interactions among surface points. Moreover, we present a new synthetic training dataset that encompasses a diverse range of shapes, materials, and illumination scenarios found in real-world scenes. Through extensive evaluation, we demonstrate that our method not only surpasses calibrated, lighting-specific techniques on public benchmarks, but also excels with a significantly smaller number of input images even without object masks.Comment: CVPR 2023 (Highlight). The source code will be available at https://github.com/satoshi-ikehata/SDM-UniPS-CVPR202