SHREC'16: partial matching of deformable shapes

Matching deformable 3D shapes under partiality transformations is a challenging problem that has received limited focus in the computer vision and graphics communities. With this benchmark, we explore and thoroughly investigate the robustness of existing matching methods in this challenging task. Participants are asked to provide a point-to-point correspondence (either sparse or dense) between deformable shapes undergoing different kinds of partiality transformations, resulting in a total of 400 matching problems to be solved for each method - making this benchmark the biggest and most challenging of its kind. Five matching algorithms were evaluated in the contest; this paper presents the details of the dataset, the adopted evaluation measures, and shows thorough comparisons among all competing methods

The role of block shape and slenderness in the preliminary estimation of rockfall propagation

Among the wide range of variables that influence the falling process of blocks during a rockfall event, the shape of the block often plays a crucial role. Spherical-like blocks typically reach longer runout distances while elongated and plate volumes stop earlier. Nevertheless, with reference to runout modelling and hazard analyses, the shape of the block was disregarded for very long time until the last two decades when more rigorous rockfall models were developed. Nowadays fully 3D rigid body models and particle-based ones can take into account different and complex aspects related to block geometry and size (e.g. shape, change of shape, slenderness, fragmentation, etc.) when in site-specific applications are addressed. On the other hand, when the rockfall analysis is extended over large areas, simplified runout models can be used for preliminary, quick analyses, aimed at highlighting the most critical zones of the area. In this case, the variables that influence the rockfall process should be included in the analysis in equivalent terms. Among these simplified models, the Cone Method allows to reduce the runout phase to an equivalent sliding motion of the block along an inclined plane. The inclination of this plane with respect to the horizontal plane (i.e. the energy angle ) can be related to both block and slope properties of the real rockfall case. The authors of this paper developed a methodology for the estimation of the energy angle as a function of the condition of the site under analysis (characteristics of the blocks and the slope), to be used for preliminary forecasting analyses at medium-small scales. To this aim, a series of parametric analyses have been carried out to quantify the role of each variable on the energy angle. In this paper, the role of block shape and slenderness (i.e. the ratio between the height and the width of the rock block) is analysed via several propagation analyses carried out on simplified synthetic slopes by using the fully 3D RAMMS::ROCKFALL model. The results were finally statistically treated in terms of energy angles in order to take into account the variability of rockfall trajectories and provide a contribution for the estimation of the parameters within preliminary analyses based on the Cone Method

Characterization of the behavior of a turbine rotor steel by inverse analysis on the small punch test

n/

Cylinders extraction in non-oriented point clouds as a clustering problem

Finding geometric primitives in 3D point clouds is a fundamental task in many engineering applications such as robotics, autonomous-vehicles and automated industrial inspection. Among all solid shapes, cylinders are frequently found in a variety of scenes, comprising natural or man-made objects. Despite their ubiquitous presence, automated extraction and fitting can become challenging if performed ”in-the-wild”, when the number of primitives is unknown or the point cloud is noisy and not oriented. In this paper we pose the problem of extracting multiple cylinders in a scene by means of a Game-Theoretic inlier selection process exploiting the geometrical relations between pairs of axis candidates. First, we formulate the similarity between two possible cylinders considering the rigid motion aligning the two axes to the same line. This motion is represented with a unitary dual-quaternion so that the distance between two cylinders is induced by the length of the shortest geodesic path in SE(3). Then, a Game-Theoretical process exploits such similarity function to extract sets of primitives maximizing their inner mutual consensus. The outcome of the evolutionary process consists in a probability distribution over the sets of candidates (ie axes), which in turn is used to directly estimate the final cylinder parameters. An extensive experimental section shows that the proposed algorithm offers a high resilience to noise, since the process inherently discards inconsistent data. Compared to other methods, it does not need point normals and does not require a fine tuning of multiple parameters

One-Shot HDR Imaging via Stereo PFA Cameras

High Dynamic Range (HDR) imaging techniques aim to increase the range of luminance values captured from a scene. The literature counts many approaches to get HDR images out of low-range camera sensors, however most of them rely on multiple acquisitions producing ghosting effects when moving objects are present. In this paper we propose a novel HDR reconstruction method exploiting stereo Polarimetric Filter Array (PFA) cameras to simultaneously capture the scene with different polarized filters, producing intensity attenuations that can be related to the light polarization state. An additional linear polarizer is mounted in front of one of the two cameras, raising the degree of polarization of rays captured by the sensor. This leads to a larger attenuation range between channels regardless the scene lighting condition. By merging the data acquired by the two cameras, we can compute the actual light attenuation observed by a pixel at each channel and derive an equivalent exposure time, producing a HDR picture from a single polarimetric shot. The proposed technique results comparable to classic HDR approaches using multiple exposures, with the advantage of being a one-shot method

Shape annotation for intelligent image retrieval

Annotation of shapes is an important process for semantic image retrieval. In this paper, we present a shape annotation framework that enables intelligent image retrieval by exploiting in a unified manner domain knowledge and perceptual description of shapes. A semi-supervised fuzzy clustering process is used to derive domain knowledge in terms of linguistic concepts referring to the semantic categories of shapes. For each category we derive a prototype that is a visual template for the category. A novel visual ontology is proposed to provide a description of prototypes and their salient parts. To describe parts of prototypes the visual ontology includes perceptual attributes that are defined by mimicking the analogy mechanism adopted by humans to describe the appearance of objects. The effectiveness of the developed framework as a facility for intelligent image retrieval is shown through results on a case study in the domain of fish shapes

Vortex pinning in Au-irradiated FeSe0.4Te0.6 crystals from the static limit to gigahertz frequencies

Fe(Se,Te) is one of the simplest compounds of iron-based superconductors, but it shows a variety of vortex pinning phenomena both in thin-film and single-crystal forms. These properties are particularly important in light of its potential for applications ranging from the development of coated conductors for high-field magnets to topological quantum computation exploiting the Majorana particles found in the superconducting vortex cores. In this paper, we characterize the pinning properties of FeSe 0.4 Te 0.6 single crystals, both pristine and Au-irradiated, with a set of characterization techniques ranging from the static limit to the GHz frequency range by using dc magnetometry, ac susceptibility measurements of both the fundamental and the third harmonic signals, and by microwave coplanar waveguide resonator measurements of London and Campbell penetration depths. We observed signatures of single vortex pinning that can be modeled by a parabolic pinning potential, dissipation caused by flux creep, and a general enhancement of the critical current density after 320 MeV Au ion irradiation

Effect of 14.1 MeV fusion neutron irradiation on YBCO thin films and commercial REBCO tapes

The design of new tokamak machines relying on the use of high temperature superconductors (HTS) is promoting the study of HTS properties at the operating conditions required by fusion applications. In particular, the interest in the damage induced by neutron irradiation on RE Ba 2 Cu 3 O 7-δ ( RE BCO, RE = Y or lanthanide series), one of the most used family of HTS, has recently risen and several studies have been devoted to radiation hardness tests performed with ion irradiation or fission neutrons. In this work, the effect of neutron irradiation on YBCO films and commercial RE BCO tapes was investigated using, for the first time, neutrons produced by the D-T fusion reaction. The experiment was carried out at ENEA-Frascati Neutron Generator (FNG) where a deuteron beam is accelerated up to 300 keV and directed on a tritiated target to produce a nearly isotropic 14.1 MeV neutron field via the T(d,n)α fusion reaction. Different YBCO films deposited through metal-organic decomposition (MOD) route on single crystals (SrTiO 3 and LaAlO 3 ) and RE BCO commercial tapes, grown by pulsed laser deposition, were irradiated. Samples exposed to three fluences were compared with a maximum neutron fluence of 1.2·10 14 cm −2 . The properties of HTS materials were assessed before and after irradiation by means of different techniques. From these measurements, no significant effect on the considered properties was recognized indicating the robustness of films up to the explored irradiation fluences

A physics-driven CNN model for real-time sea waves 3D reconstruction

One of the most promising techniques for the analysis of Spatio-Temporal ocean wave fields is stereo vision. Indeed, the reconstruction accuracy and resolution typically outperform other approaches like radars, satellites, etc. However, it is computationally expensive so its application is typically restricted to the analysis of short pre-recorded sequences. What prevents such methodology from being truly real-time is the final 3D surface estimation from a scattered, non-equispaced point cloud. Recently, we studied a novel approach exploiting the temporal dependence of subsequent frames to iteratively update the wave spectrum over time. Albeit substantially faster, the unpre-dictable convergence time of the optimization involved still prevents its usage as a continuously running remote sensing infrastructure. In this work, we build upon the same idea, but investigat-ing the feasibility of a fully data-driven Machine Learning (ML) approach. We designed a novel Convolutional Neural Network that learns how to produce an accurate surface from the scattered elevation data of three subsequent frames. The key idea is to embed the linear dispersion relation into the model itself to physically relate the sparse points observed at different times. Assuming that the scattered data are uniformly distributed in the spatial domain, this has the same effect of increasing the sample density of each single frame. Experiments demonstrate how the proposed technique, even if trained with purely synthetic data, can produce accurate and physically consistent surfaces at five frames per second on a modern PC