Unpaired Depth Super-Resolution in the Wild

Depth maps captured with commodity sensors are often of low quality and resolution; these maps need to be enhanced to be used in many applications. State-of-the-art data-driven methods of depth map super-resolution rely on registered pairs of low- and high-resolution depth maps of the same scenes. Acquisition of real-world paired data requires specialized setups. Another alternative, generating low-resolution maps from high-resolution maps by subsampling, adding noise and other artificial degradation methods, does not fully capture the characteristics of real-world low-resolution images. As a consequence, supervised learning methods trained on such artificial paired data may not perform well on real-world low-resolution inputs. We consider an approach to depth super-resolution based on learning from unpaired data. While many techniques for unpaired image-to-image translation have been proposed, most fail to deliver effective hole-filling or reconstruct accurate surfaces using depth maps. We propose an unpaired learning method for depth super-resolution, which is based on a learnable degradation model, enhancement component and surface normal estimates as features to produce more accurate depth maps. We propose a benchmark for unpaired depth SR and demonstrate that our method outperforms existing unpaired methods and performs on par with paired

Institutional factors of labour potential development in the context of financial support for transformation of social infrastructure

The development of labour potential is determined by the conditions and factors influencing the formation of its quantitative and qualitative characteristics. In modern conditions, the priority objective of the state is to increase the quantitative level of labour potential; therefore, research into the causes of the demographic problem is necessary to identify effective social, economic and financial mechanisms which can be applied to solve it. The paper presents methods of financial support for the development of social infrastructure as one of the main factors in the formation of favourable social and living conditions for the population; the characteristic features of the mechanism for financing social expenditures in the regions of the Central Federal District are considered. An institutional approach is made use of to identify factors for the development of labour potential and its elements, and to offer promising mechanisms for financial support for the development of social infrastructure. An analysis of the methodological foundations of financing for social infrastructure will contribute to solution for the problems of state social policy based on increasing the efficiency of public spending. In addition, research into the criteria for financing the social sphere will make it possible to determine the degree of influence of various financial methods on the development of the social infrastructure. General logical, descriptive methods were used, and a systematic approach had the predominant role in the research. Official statistical information was processed with the use of mathematical statistics tools and interpreted with the help of general scientific methods of analysis

SIMULATION OF THE TRAFFIC PROCESS ON THE MAIN STREET OF VORONEZH IN THE ANY LOGIC SOFTWARE ENVIRONMENT

When carrying out any activities related to the organization of traffic, aimed primarily at improving the efficiency of the road network, it is very important to evaluate the measures taken or being taken at the design stage. In such cases, specialists quite often have to use various kinds of simulation products, especially often their use occurs when evaluating methods or modes of traffic control. Despite the large number of simulation products that exist today, the Any Logic simulation package is especially popular. To determine the main blocks used to perform the procedure under consideration and evaluate the effectiveness of the main street of Voronezh - Leninskiy Prospekt, within the framework of the article, a modeling procedure was performed and the effectiveness of the product used was evaluated based on the result of comparing the traffic flow indicator.</jats:p

Unpaired Depth Super-Resolution in the Wild

Depth images captured with commodity sensors commonly suffer from low quality and resolution and require enhancing to be used in many applications. State-of-the-art data-driven methods for depth super-resolution rely on registered pairs of low- and high-resolution depth images of the same scenes. Acquisition of such real-world paired data requires specialized setups. On the other hand, generating low-resolution depth images from respective high-resolution versions by subsampling, adding noise and other artificial degradation methods, does not fully capture the characteristics of real-world depth data. As a consequence, supervised learning methods trained on such artificial paired data may not perform well on real-world low-resolution inputs. We propose an approach to depth super-resolution based on learning from unpaired data. We show that image-based unpaired techniques that have been proposed for depth super-resolution fail to perform effective hole-filling or reconstruct accurate surface normals in the output depth images. Aiming to improve upon these approaches, we propose an unpaired learning method for depth super-resolution based on a learnable degradation model and including a dedicated enhancement component which integrates surface quality measures to produce more accurate depth images. We propose a benchmark for unpaired depth super-resolution and demonstrate that our method outperforms existing unpaired methods and performs on par with paired ones. In particular, our method shows 28% improvement in terms of a perceptual MSEv quality measure, compared to state-of-the-art unpaired depth enhancement techniques adapted to perform super-resolution [e.g., Gu et al. (2020)]. The implementation of our method is publicly available at https://github.com/keqpan/udsr

Multi-sensor large-scale dataset for multi-view 3D reconstruction

We present a new multi-sensor dataset for 3D surface reconstruction. It includes registered RGB and depth data from sensors of different resolutions and modalities: smartphones, Intel RealSense, Microsoft Kinect, industrial cameras, and structured-light scanner. The data for each scene is obtained under a large number of lighting conditions, and the scenes are selected to emphasize a diverse set of material properties challenging for existing algorithms. In the acquisition process, we aimed to maximize high-resolution depth data quality for challenging cases, to provide reliable ground truth for learning algorithms. Overall, we provide over 1.4 million images of 110 different scenes acquired at 14 lighting conditions from 100 viewing directions. We expect our dataset will be useful for evaluation and training of 3D reconstruction algorithms of different types and for other related tasks. Our dataset and accompanying software will be available online

Modern Legal Education: Traditions and Innovations of O.E.Kutafin University (MSLA)

The collective monograph is an anniversary edition dedicated to the 90th anniversary of the Kutafin Moscow State Law University (MSLA), and reflects the vision of the modern model of legal education, analysis and forecasting of trends in the development of higher education based on the traditions of fundamental legal education and innovative practices accumulated by the University.&#x0D; For scientific and pedagogical workers, administrative and managerial personnel of educational organizations that train lawyers, as well as for a wide range of readers interested in innovations in the field of education.</jats:p