Loss-resilient Coding of Texture and Depth for Free-viewpoint Video Conferencing

Free-viewpoint video conferencing allows a participant to observe the remote 3D scene from any freely chosen viewpoint. An intermediate virtual viewpoint image is commonly synthesized using two pairs of transmitted texture and depth maps from two neighboring captured viewpoints via depth-image-based rendering (DIBR). To maintain high quality of synthesized images, it is imperative to contain the adverse effects of network packet losses that may arise during texture and depth video transmission. Towards this end, we develop an integrated approach that exploits the representation redundancy inherent in the multiple streamed videos a voxel in the 3D scene visible to two captured views is sampled and coded twice in the two views. In particular, at the receiver we first develop an error concealment strategy that adaptively blends corresponding pixels in the two captured views during DIBR, so that pixels from the more reliable transmitted view are weighted more heavily. We then couple it with a sender-side optimization of reference picture selection (RPS) during real-time video coding, so that blocks containing samples of voxels that are visible in both views are more error-resiliently coded in one view only, given adaptive blending will erase errors in the other view. Further, synthesized view distortion sensitivities to texture versus depth errors are analyzed, so that relative importance of texture and depth code blocks can be computed for system-wide RPS optimization. Experimental results show that the proposed scheme can outperform the use of a traditional feedback channel by up to 0.82 dB on average at 8% packet loss rate, and by as much as 3 dB for particular frames

Reconstruction of neuronal activity and connectivity patterns in the zebrafish olfactory bulb

In the olfactory bulb (OB), odors evoke distributed patterns of activity across glomeruli that are reorganized by networks of interneurons (INs). This reorganization results in multiple computations including a decorrelation of activity patterns across the output neurons, the mitral cells (MCs). To understand the mechanistic basis of these computations it is essential to analyze the relationship between function and structure of the underlying circuit. I combined in vivo twophoton calcium imaging with dense circuit reconstruction from complete serial block-face electron microscopy (SBEM) stacks of the larval zebrafish OB (4.5 dpf) with a voxel size of 9x9x25nm. To address bottlenecks in the workflow of SBEM, I developed a novel embedding and staining procedure that effectively reduces surface charging in SBEM and enables to acquire SBEM stacks with at least a ten-fold increase in both, signal-to-noise as well as acquisition speed. I set up a high throughput neuron reconstruction pipeline with >30 professional tracers that is available for the scientific community (ariadne-service.com). To assure efficient and accurate circuit reconstruction, I developed PyKNOSSOS, a Python software for skeleton tracing and synapse annotation, and CORE, a skeleton consolidation procedure that combines redundant reconstruction with targeted expert input. Using these procedures I reconstructed all neurons (>1000) in the larval OB. Unlike in the adult OB, INs were rare and appeared to represent specific subtypes, indicating that different sub-circuits develop sequentially. MCs were uniglomerular whereas inter-glomerular projections of INs were complex and biased towards groups of glomeruli that receive input from common types of sensory neurons. Hence, the IN network in the OB exhibits a topological organization that is governed by glomerular identity. Calcium imaging revealed that the larval OB circuitry already decorrelates activity patterns evoked by similar odors. The comparison of inter-glomerular connectivity to the functional interactions between glomeruli indicates that pattern decorrelation depends on specific, non-random inter-glomerular IN projections. Hence, the topology of IN networks in the OB appears to be an important determinant of circuit function

A software tool for the semi-automatic segmentation of architectural 3D models with semantic annotation and Web fruition

The thorough documentation of Cultural Heritage artifacts is a fundamental concern for management and preservation. In this context, the semantic segmentation and annotation of 3D models of historic buildings is an important modern topic. This work describes a software tool currently under development, for interactive and semi-automatic segmentation, characterization, and annotation of 3D models produced by photogrammetric surveys. The system includes some generic and well-known segmentation approaches, such as region growing and Locally Convex Connected Patches segmentation, but it also contains original code for specific semantic segmentation of parts of buildings, in particular straight stairs and circular-section columns. Furthermore, a method for automatic wall-surface characterization is devoted to rusticated-ashlar detection, in view of masonry-unit segmentation. The software is modular, so allowing easy expandability. It also has tools for data encoding into formats ready for model fruition by Web technologies. These results were partly obtained in collaboration with Corvallis SPA (Padua-Italy, http://www.corvallis.it)

Обеспечение визуальной когерентности в обучающих системах дополненной реальности с учетом авиакосмической специфики

In May 2022, Saudi Arabian Military Industries, a Saudi government agency, acquired an augmented reality training platform for pilots. In September, the Boeing Corporation began the development of an augmented reality pilot simulator. In November, a similar project was launched by BAE Systems, a leading British developer of aeronautical engineering. These facts allow us to confidently speak about the beginning of a new era of aviation simulators – simulators using the augmented reality technology. One of the promising advantages of this technology is the ability to safely simulate dangerous situations in the real world. A necessary condition for using this advantage is to ensure the visual coherence of augmented reality scenes: virtual objects must be indistinguishable from real ones. All the global IT leaders consider augmented reality as the subsequent surge of radical changes in digital electronics, so visual coherence is becoming a key issue for the future of IT, and in aerospace applications, visual coherence has already acquired practical significance. The Russian Federation lags far behind in studying the problems of visual coherence in general and for augmented reality flight simulators in particular: at the time of publication the authors managed to find only two papers on the subject in the Russian research space, while abroad their number is already approximately a thousand. The purpose of this review article is to create conditions for solving the problem. Visual coherence depends on many factors: lighting, color tone, shadows from virtual objects on real ones, mutual reflections, textures of virtual surfaces, optical aberrations, convergence and accommodation, etc. The article reviews the publications devoted to methods for assessing the conditions of illumination and color tone of a real scene and transferring them to virtual objects using various probes and by individual images, as well as by rendering virtual objects in augmented reality scenes, using neural networks.В мае 2022 года саудовская правительственная структура Saudi Arabian Military Industries приобрела обучающую платформу дополненной реальности для летчиков, в сентябре корпорация Boeing начала разработку тренажера пилота дополненной реальности, в ноябре стартовал аналогичный проект ведущего британского разработчика авиационной техники BAE Systems. Эти факты позволяют уверенно говорить о начале новой эпохи авиационных тренажеров – тренажеров с применением технологии дополненной реальности. Одно из перспективных преимуществ данной технологии – возможность безопасного моделирования опасных ситуаций в реальном мире. Необходимым условием использования этого преимущества является обеспечение визуальной когерентности сцен дополненной реальности: виртуальные объекты должны быть неотличимы от реальных. Все мировые IT-лидеры рассматривают дополненную реальность как следующую «большую волну» радикальных изменений в цифровой электронике, поэтому визуальная когерентность становится ключевым вопросом для будущего IT, а в аэрокосмических приложениях визуальная когерентность уже приобрела практическое значение. В РФ имеет место серьезное отставание в изучении проблематики визуальной когерентности в целом и для авиатренажеров дополненной реальности в частности: на момент публикации авторам удалось обнаружить в российском научном пространстве только две работы по теме, тогда как за рубежом их число уже около тысячи. Цель настоящей обзорной статьи – создать условия для купирования проблемы. Визуальная когерентность зависит от многих факторов: освещения, цветового тона, теней от виртуальных объектов на реальных, взаимных отражений, текстур виртуальных поверхностей, оптических аберраций, конвергенции и аккомодации и др. В статье анализируются публикации, посвященные методам оценки условий освещенности и цветового тона реальной сцены и переноса таковых на виртуальные объекты с использованием зондов и по отдельным изображениям, а также по рендерингу виртуальных объектов в сценах дополненной реальности, в том числе с применением нейросетей

Modelling of building interiors with mobile phone sensor data

Creating as-built plans of building interiors is a challenging task. In this paper we present a semi-automatic modelling system for creating residential building interior plans and their integration with existing map data to produce building models. Taking a set of imprecise measurements made with an interactive mobile phone room mapping application, the system performs spatial adjustments in accordance with soft and hard constraints imposed on the building plan geometry. The approach uses an optimisation model that exploits a high accuracy building outline, such as can be found in topographic map data, and the building topology to improve the quality of interior measurements and generate a standardised output. We test our system on building plans of five residential homes. Our evaluation shows that the approach enables construction of accurate interior plans from imprecise measurements. The experiments report an average accuracy of 0.24 m, close to the 0.20 m recommended by the CityGML LoD4 specificatio

Three-Dimensional Microscopic Image Reconstruction Based on Structured Light Illumination

In this paper, we propose and experimentally demonstrate a three-dimensional (3D) microscopic system that reconstructs a 3D image based on structured light illumination. The spatial pattern of the structured light changes according to the profile of the object, and by measuring the change, a 3D image of the object is reconstructed. The structured light is generated with a digital micro-mirror device (DMD), which controls the structured light pattern to change in a kHz rate and enables the system to record the 3D information in real time. The working distance of the imaging system is 9 cm at a resolution of 20 μm. The resolution, working distance, and real-time 3D imaging enable the system to be applied in bridge and road crack examinations, and structure fault detection of transportation infrastructures

Rough or Noisy? Metrics for Noise Estimation in SfM Reconstructions

Structure from Motion (SfM) can produce highly detailed 3D reconstructions, but distinguishing real surface roughness from reconstruction noise and geometric inaccuracies has always been a difficult problem to solve. Existing SfM commercial solutions achieve noise removal by a combination of aggressive global smoothing and the reconstructed texture for smaller details, which is a subpar solution when the results are used for surface inspection. Other noise estimation and removal algorithms do not take advantage of all the additional data connected with SfM. We propose a number of geometrical and statistical metrics for noise assessment, based on both the reconstructed object and the capturing camera setup. We test the correlation of each of the metrics to the presence of noise on reconstructed surfaces and demonstrate that classical supervised learning methods, trained with these metrics can be used to distinguish between noise and roughness with an accuracy above 85%, with an additional 5–6% performance coming from the capturing setup metrics. Our proposed solution can easily be integrated into existing SfM workflows as it does not require more image data or additional sensors. Finally, as part of the testing we create an image dataset for SfM from a number of objects with varying shapes and sizes, which are available online together with ground truth annotations

Human Pose Estimation from Monocular Images : a Comprehensive Survey

Human pose estimation refers to the estimation of the location of body parts and how they are connected in an image. Human pose estimation from monocular images has wide applications (e.g., image indexing). Several surveys on human pose estimation can be found in the literature, but they focus on a certain category; for example, model-based approaches or human motion analysis, etc. As far as we know, an overall review of this problem domain has yet to be provided. Furthermore, recent advancements based on deep learning have brought novel algorithms for this problem. In this paper, a comprehensive survey of human pose estimation from monocular images is carried out including milestone works and recent advancements. Based on one standard pipeline for the solution of computer vision problems, this survey splits the problema into several modules: feature extraction and description, human body models, and modelin methods. Problem modeling methods are approached based on two means of categorization in this survey. One way to categorize includes top-down and bottom-up methods, and another way includes generative and discriminative methods. Considering the fact that one direct application of human pose estimation is to provide initialization for automatic video surveillance, there are additional sections for motion-related methods in all modules: motion features, motion models, and motion-based methods. Finally, the paper also collects 26 publicly available data sets for validation and provides error measurement methods that are frequently used