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No 3 (2023)

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COMPUTER GRAPHICS AND VISUALIZATION

SUPPORTING VECTOR TEXTURES IN A GPU PHOTOREALISTIC RENDERING SYSTEM

SANZHAROV V.V., FROLOV V.A., GALAKTIONOV V.A.

Abstract

Images in vector format are presented as a sequence of analytical descriptions of geometric objects. This approach allows for reproduction of the image in any resolution without loss of quality. Currently, there are no ready-made solutions for using vector images in GPU photorealistic rendering systems. This paper presents an approach to enabling such support using signed distance fields and rasterization as base methods. Analysis of the results shows the effectiveness of the approach based on distance fields for various vector images. However, in some cases, artifacts may appear, in which case it is proposed to use a rasterization-based approach.

Programmirovanie. 2023;(3):3-12
pages 3-12 views

AN ALGORITHM FOR DETECTING PRECIPITATION IN COMPUTER PROCESSING OF VIDEO IMAGES

DMITRIEV V.T., BAUKOV A.A.

Abstract

The importance of detecting and reducing the visibility of precipitation in video images obtained by fixed cameras is shown. A statistical analysis of the geometric (area, shape factor, and orientation deviation from the frame average), and color–brightness (intensity and color saturation) characteristics of rain and snow particles is performed in order to substantiate decision rules for detecting pixels corresponding to precipitation particles. This analysis consists in obtaining distributions of the particle parameters and approximating them by known distribution laws using the family of Pearson’s curves, the Kolmogorov criterion, and the Nelder–Mead simplex algorithm. An algorithm for detecting raindrops and snowflakes in video sequences is developed, which is supposed to be used as part of an algorithm for reducing the visibility of precipitation. The proposed approach is presented in the form of a multistage classification of frame pixels into zones with moving objects and regions of a stationary background distorted and undistorted by precipitation particles in accumulated frames. Depending on the region to which the processed pixel belongs, the final decision to assign it to the class of precipitation is made using the proposed decision rules or the developed thresholding procedure with automatic determination of local threshold values. The proposed algorithm is experimentally investigated and, using a two-criteria approach, the optimal values for the number of accumulated frames for the correct operation of the algorithm are determined—100 frames for video images with rain and 140 frames for video with snow. The gain of the developed approach in comparison with the known estimates of the probabilities of false positives and false negatives is up to 1.7% and 9.1%, respectively.

Programmirovanie. 2023;(3):13-25
pages 13-25 views

3D SCENE RECONSTRUCTION AND DIGITIZATION METHOD FOR MIXED REALITY SYSTEMS

SOROKIN M.I., ZHDANOV D.D., ZHDANOV A.D.

Abstract

Mixed reality systems are a promising direction of research that opens up great opportunities for
interaction with virtual objects in the real world. Like any promising direction, mixed reality has a number of unresolved problems. One of these problems is the synthesis of natural lighting conditions for virtual objects, including the correct light interaction of virtual objects with the real world. Since virtual and real objects are located in different spaces, it is difficult to ensure their correct interaction. To create digital copies of realworld objects, machine learning tools and neural network technologies are employed. These methods are successfully used in computer vision for space orientation and environment reconstruction. As a solution, it is proposed to transfer all objects into the same information space: virtual space. This makes it possible to solve most of the problems associated with visual discomfort caused by the unnatural light interaction of real and virtual objects. Thus, the basic idea of the method is to recognize physical objects from point clouds and replace these objects with virtual CAD models. In other words, it implies semantic analysis of a scene and classification of objects with their subsequent transformation into polygonal models. In this study, we use competitive neural network architectures, which can provide state-of-the-art results. The test experiments are carried out on Semantic3D, ScanNet, and S3DIS, which are currently the largest datasets with point clouds that represent indoor scenes. For semantic segmentation and classification of 3D point clouds, we use the PointNeXt architecture based on PointNet, as well as modern methods of data augmentation in the process of learning. For geometry reconstruction, the Soft Rasterizer differentiable rendering method and the Total3Understanding neural network are considered.

Programmirovanie. 2023;(3):26-36
pages 26-36 views

USING MULTILEVEL HASH TABLES TO SPEED UP RENDERING

ZHDANOV D.D., LYSYKH A.I., KHALIMOV R.R., KINEV I.E., ZHDANOV A.D.

Abstract

In this paper, we analyze realistic rendering methods in terms of their efficiency in calculating caustic and indirect illumination. As the basic approach to realistic rendering, we choose bidirectional progressive ray tracing with backward photon maps. The main factors that reduce the efficiency of this method are analyzed. It is shown that the main factor that affects its performance is slow access to photon map data. Different techniques for construction of spatial acceleration structures are considered, their advantages and disadvantages are investigated. As the main approaches, we select the regular spatial grid and binary kd tree. The spatial grid provides high-speed access to photon data at low adaptability of photon map partitioning. The kd tree is characterized by high spatial adaptability of photon map partitioning but slow access to photon data. We propose a combined solution that takes advantage of the adaptability of the kd tree and the fast data access of the spatial grid. For this purpose, the regular grid is superimposed on the kd tree constructed based on the principle of space partitioning of a photon region into geometrically identical halves. To reduce memory consumption, we propose, first, to use multilevel spatial grids superimposed on the selected nodes of the kd tree and, second, to store spatial grids in the form of hash tables in order to reduce the size of the acceleration structure. Thus, a spatial acceleration structure of a new type—a tree of hash tables—is proposed and implemented. For the spatial structure developed, we implement methods for finding the nearest photons the integration spheres of which cover the illumination point, as well as methods for finding the intersection between a ray segment and photon integration spheres. The proposed software solutions are implemented in the Lumicept software package; for some scenes, the proposed method is compared with the Lumicept method based on the binary tree. The comparison shows that our method can increase the overall speed of the rendering process by more than 40%.

Programmirovanie. 2023;(3):37-48
pages 37-48 views

AUTOMATED METHOD FOR OPTIMUM SCALE SEARCH WHEN USING TRAINED MODELS FOR HISTOLOGICAL IMAGE ANALYSIS

PENKIN M.A., KHVOSTIKOV A.V., KRYLOV A.S.

Abstract

Preparation of input data for an artificial neural network is a key step to achieve a high accuracy of its predictions. It is well known that convolutional neural models have low invariance to changes in the scale of input data. For instance, processing multiscale whole-slide histological images by convolutional neural networks naturally poses a problem of choosing an optimal processing scale. In this paper, this problem is solved by iterative analysis of distances to a separating hyperplane that are generated by a convolutional classifier at different input scales. The proposed method is tested on the DenseNet121 deep architecture pretrained on PATH-DT-MSU data, which implements patch classification of whole-slide histological images.

Programmirovanie. 2023;(3):49-55
pages 49-55 views

AN EFFICIENT TECHNOLOGY OF REAL-TIME MODELING OF HEIGHT FIELD SURFACE ON THE RAY TRACING PIPELINE

TIMAKOV P.Y., MIKHAYLYUK M.V.

Abstract

In this paper, based on height field surface example, an efficient technology of real-time modeling of complex procedural objects on the ray tracing pipeline (RT-pipeline) is proposed. The proposed technology doesn’t overload the I-shader stage (intersection shader), but distributes the computational load between the I-shader and the AH-shader (any-hit shader). The key innovations of the technology are the early rejection at the I-shader stage of the bounding boxes (AABBs) extracted by the RT-pipeline hardware unit, and the “transparent AABB” concept which allows transferring costly computing of the “ray-procedural object” intersection to a later AH-shader stage. The paper also describes a number of modifications that reduce the amount of such calculations. The proposed technology was implemented in a software complex in C++, GLSL and using the Vulkan API. The performance of the developed solution was studied under various ray tracing conditions on the task of modeling the surface of a detailed Puget Sound height field. The obtained results confirmed high efficiency of the developed technology and the possibility of its application in virtual environment systems, simulators, scientific visualization, etc.

Programmirovanie. 2023;(3):56-64
pages 56-64 views

DATA ANALYSIS

AUGMENTING THE TRAINING SET OF HISTOLOGICAL IMAGES WITH ADVERSARIAL EXAMPLES

LOKSHIN N.D., KHVOSTIKOV A.V., KRYLOV A.S.

Abstract

In this paper, we consider the problem of augmenting a set of histological images with adversarial examples to improve the robustness of the neural network classifiers trained on the augmented set against adversarial attacks. In recent years, neural network methods have been developed rapidly, achieving impressive results. However, they are subjected to the so-called adversarial attacks; i.e., they make incorrect predictions on input images with added imperceptible noise. Hence, the reliability of neural network methods remains an important area of research. In this paper, we compare different methods for training set augmentation to improve the robustness of neural histological image classifiers against adversarial attacks. For this purpose, we augment the training set with adversarial examples generated by several popular methods.

Programmirovanie. 2023;(3):65-70
pages 65-70 views

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