Rasterization Algorithm Research Articles

Separated spacecraft attitude algorithm research based on star sensor multi-information fusion

Faced with the problem of poor anti-interference ability of star map recognition technology, in order to improve its anti-interference ability and achieve higher attitude measurement accuracy, this article proposes a star map recognition method based on binary search trees. Star sensors and gyroscopes are combined to solve the problem of the poor real-time performance of star sensors, optimizing the Extended Kalman Filtering (EKF) algorithm. This improves attitude output accuracy and formulates corresponding attitude measurement schemes considering the situation where the star sensor is not visible. The results showed that the improved star pattern recognition algorithm had better adaptability and robustness than the rasterization algorithm, with a high recognition success rate of 95.6% when the star sensor field of view was 3°*3°, while the rasterization algorithm cannot recognize small fields of view. The recognition success rate of the algorithm was 98.6% when the standard deviation of magnitude noise was 2.0 pixels, which was 15.4% higher than the rasterization algorithm. Unlike the EKF algorithm, the optimized EKF algorithm had a higher attitude output accuracy, with an average error of about 0.000 211°; compared to before optimization, it has increased by 93.43%. In the attitude measurement scheme, the relative attitude angle measurement error was small, and the maximum attitude measurement error in the rolling angle direction was 1.8 × 10−3°. This paper adopts a method to achieve high-precision satellite attitude measurement with good adaptability, which can be applied in complex space missions.

McRPL: a general purpose parallel raster processing library on distributed heterogeneous architectures

Parallel computing on distributed heterogeneous architectures (e.g. computing clusters with multiple CPUs and GPUs) can significantly improve the computational efficiency and scalability of complicated algorithms, but it is theoretically and technically complex. Parallel raster processing libraries reduce the development complexity of parallel raster algorithms by hiding parallel computing details; however, no existing library sufficiently utilizes distributed heterogeneous computing resources. A general-purpose raster processing library (mcRPL) combining multi-process parallelism and multi-thread parallelism is proposed to enable parallel raster processing on distributed heterogeneous architectures with multiple CPUs and GPUs. Additionally, an adaptive hardware assignment strategy is proposed to fully utilize available processors in various hardware environments. A series of task-processing strategies are adopted to aim toward maximizing the utilization of the computing capacity of involved processors. Experiments revealed that two raster algorithms parallelized using mcRPL for spatiotemporal data fusion and land-use change simulation were 170.7- and 143.2-fold faster than original serial algorithms using 8 and 16 GPUs, respectively. While hiding the details of mixed parallelism and reducing the development complexity, mcRPL provides user-friendly interfaces for the development of parallel raster algorithms to enhance computational performance and enable large-scale raster computing tasks with extensive data volumes.

BowTie rasterization for extreme multi-view light-field displays

The light-field display (LfD) radiance image is a raster description of a light-field where every pixel in the image represents a unique ray within a 3D volume. The LfD radiance image can be projected through an array of micro-lenses to project a perspective-correct 3D aerial image visible for all viewers within the LfDs projection frustum. The synthetic LfD radiance image is comparable to the radiance image as captured by a plenoptic/light-field camera but is rendered from a 3D model or scene. Synthetic radiance image rasterization is an example of extreme multi-view rendering as the 3D scene must be rendered from many (1,000s to millions) viewpoints into small viewports per update of the light-field display. However, GPUs and their accompanying APIs (OpenGL, DirectX, Vulkan) generally expect to render a 3D scene from one viewpoint to a single large viewport/framebuffer. Therefore, LfD radiance image rendering is extremely time consuming and compute intensive. This paper reviews the novel, full-parallax, BowTie Radiance Image Rasterization algorithm which can be embedded within an LfD to accelerate light-field radiance image rendering for real-time update.

An improved midpoint rasterization algorithm implemented in FPGA

With the continuous progress of technology and the improvement of people's consumption level, requirements for picture quality are constantly increasing, which has raised the requirements for graphic processing units(GPU) and promoted their development. Among functional modules of GPU, the rasterization module is an important part of GPU. In this paper, we first review the development of rasterization algorithms and analyze their advantages and disadvantages. After that, we improve classical midpoint traversal by adding the idea of tile scanning and improving the parallelism of the algorithm and propose a tile-based multi-parallel midpoint traversal. Then, we implement and verify the proposed algorithm on software and hardware and compare the performance of the proposed algorithm with other algorithms to verify the superiority of the proposed algorithm. Finally, we deploy the improved algorithm in FPGA to verify the feasibility of the hardware.

Multidirectional Shift Rasterization (MDSR) Algorithm for Effective Identification of Ground in Dense Point Clouds

With the ever-increasing popularity of unmanned aerial vehicles and other platforms providing dense point clouds, filters for the identification of ground points in such dense clouds are needed. Many filters have been proposed and are widely used, usually based on the determination of an original surface approximation and subsequent identification of points within a predefined distance from such surface. We presented a new filter, the multidirectional shift rasterization (MDSR) algorithm, which is based on a different principle, i.e., on the identification of just the lowest points in individual grid cells, shifting the grid along both the planar axis and subsequent tilting of the entire grid. The principle was presented in detail and both visually and numerically compared with other commonly used ground filters (PMF, SMRF, CSF, and ATIN) on three sites with different ruggedness and vegetation density. Visually, the MDSR filter showed the smoothest and thinnest ground profiles, with the ATIN the only filter comparably performing. The same was confirmed when comparing the ground filtered by other filters with the MDSR-based surface. The goodness of fit with the original cloud is demonstrated by the root mean square deviations (RMSDs) of the points from the original cloud found below the MDSR-generated surface (ranging, depending on the site, between 0.6 and 2.5 cm). In conclusion, this paper introduced a newly developed MDSR filter that outstandingly performed at all sites, identifying the ground points with great accuracy while filtering out the maximum of vegetation and above-ground points and outperforming the aforementioned widely used filters. The filter dilutes the cloud somewhat; in such dense point clouds, however, this can be perceived as a benefit rather than as a disadvantage.

Tile-based multiple parallel triangle rasterization algorithm implemented in FPGA

With the progress of rendering technology, the industry has put forward higher requirements for GPU performance while rasterization algorithm plays an important role in GPU performance. In this paper, we first review some of the current mainstream rasterization algorithms. Then we propose a triangle rasterization algorithm—tile-based multiple parallel midpoint rasterization algorithm. This algorithm combines the characteristics of midpoint traversal and tiled traversal. At last, we use FPGA to implement the algorithm in order to verify the feasibility of the algorithm. And we use the triangle data set used in previous papers to simulate in Modelsim software. The results show that this algorithm can improve the rasterization speed and the highest frequency of this design is 200MHZ.

Aerial Survey Robotics in Extreme Environments: Mapping Volcanic CO2 Emissions With Flocking UAVs

We present methods for autonomous collaborative surveying of volcanic CO2 emissions using aerial robots. CO2 is a useful predictor of volcanic eruptions and an influential greenhouse gas. However, current CO2 mapping methods are hazardous and inefficient, as a result, only a small fraction of CO2 emitting volcanoes have been surveyed. We develop algorithms and a platform to measure volcanic CO2 emissions. The Dragonfly Unpiloted Aerial Vehicle (UAV) platform is capable of long-duration CO2 collection flights in harsh environments. We implement two survey algorithms on teams of Dragonfly robots and demonstrate that they effectively map gas emissions and locate the highest gas concentrations. Our experiments culminate in a successful field test of collaborative rasterization and gradient descent algorithms in a challenging real-world environment at the edge of the Valles Caldera supervolcano. Both algorithms treat multiple flocking UAVs as a distributed flexible instrument. Simultaneous sensing in multiple UAVs gives scientists greater confidence in estimates of gas concentrations and the locations of sources of those emissions. These methods are also applicable to a range of other airborne concentration mapping tasks, such as pipeline leak detection and contaminant localization.

Statistics-Based Chain Code Compression with Decreased Sensitivity to Shape Artefacts

Chain codes compactly represent raster curves, but there is still a lot of room for improvement by means of data compression. Several statistics-based chain code compression techniques assign shorter extra codes to frequent pairs of consecutive symbols. Here we systematically extend this concept to patterns of up to six symbols. A curve may be represented by any of the exponentially many overlapped chains of codes, and the dynamic programming approach is proposed to determine the optimal chain. We also propose utilization of multiple averaged hard coded pseudo-statistical models, since the exact statistical models of individual curves are often huge, and they can also significantly differ from each other. A competitive compression efficiency is assured in this manner and, as a pleasant side effect, this efficiency is less affected by the curve’s shape, rasterization algorithm, noise, and image resolution, than in other contemporary methods, which surprisingly do not pay any attention to this problem at all.

The Wolf pack assignment rule based on ant colony algorithm and the path planning of scout ants in complex raster diagra

The traditional ant colony algorithm based on raster algorithm has slow searching speed and convergence speed in the problem of robot path planning, and it is easy to fall into the problem of local optimal. In order to solve this problem, an improved ant colony algorithm based on Wolf distribution rules and reconnaissance ant strategy is proposed, which can adjust the pheromone coefficient adaptively and obtain a more suitable proportion of pheromone allocation. Experiments show that this algorithm is superior to the traditional ant colony algorithm in terms of optimal path searching speed, quality and convergence speed in simple and complex environments.

Parallel multi-view polygon rasterization for 3D light field display.

Three-dimensional (3D) light field displays require samples of image data captured from a large number of regularly spaced camera images to produce a 3D image. Generally, it is inefficient to generate these images sequentially because a large number of rendering operations are repeated in different viewpoints. The current 3D image generation algorithm with traditional single viewpoint computer graphics techniques is not sufficiently well suited to the task of generating images for the light field displays. A highly parallel multi-view polygon rasterization (PMR) algorithm for 3D multi-view image generation is presented. Based on the coherence of the triangular rasterization calculation among different viewpoints, the related rasterization algorithms including primitive setup, plane function, and barycentric coordinate interpolation in the screen space are derived. To verify the proposed algorithm, a hierarchical soft rendering pipeline with GPU is designed and implemented. Several groups of images of 3D objects are used to verify the performance of the PMR method, and the correct 3D light field image can be achieved in real time.

High-efficient extraction of drainage networks from digital elevation models constrained by enhanced flow enforcement from known river maps

Drainage network extraction plays an important role in geomorphologic analyses, hydrologic modeling, and non-point source pollutant simulation, among others. Flow enforcement, by imposing information of known river maps to digital elevation models (DEMs), leads to improved drainage network extraction. However, the existing flow enforcement methods (e.g., the elevation-based stream-burning method) have certain limitations, as they may cause unreal longitudinal profiles, lead to unintended topological errors, and even misinterpret the overall drainage patterns. The present study proposes an enhanced flow enforcement method without elevation modification towards an accurate and efficient drainage network extraction. In addition to preserving the Boolean-value information as to whether a DEM pixel belongs to a stream, the proposed method can also well preserve and fully utilize the topological relations among mapped streamlines and morphological information of each mapped streamline. The method involves two important steps: (1) proposal of an improved rasterization algorithm of mapped streamlines to yield continuous, unambiguous, and collision-free raster equivalent of stream vectors for flow enforcement; and (2) realization of the enhanced flow enforcement in a modified Priority-Flood procedure –– in this way, flows are enforced to completely follow the mapped streamlines, and hence, channel short-circuits and spurious confluences of adjacent streams are avoided. An efficient implementation of the method is made based on a size-balanced binary search tree. The method is also tested over the Rogue River Basin in the United States, using DEMs with various resolutions. Visual and statistical analyses of the results indicate three major advantages of the proposed method: (1) significant reduction in the misinterpretation of drainage patterns; (2) maximum channel displacement of one pixel to the river map at various resolutions; and (3) high computational efficiency.

Tile Pair-Based Adaptive Multi-Rate Stereo Shading.

This work proposes a new stereo shading architecture that enables adaptive shading rates and automatic shading reuse among triangles and between two views. The proposed pipeline presents several novel features. First, the present sort-middle/bin shading is extended to tile pair-based shading to rasterize and shade pixels at two views simultaneously. A new rasterization algorithm utilizing epipolar geometry is then proposed to schedule tile pairs and perform rasterization at stereo views efficiently. Second, this work presents an adaptive multi-rate shading framework to compute shading on pixels at different rates. A novel tile-based screen space cache and a new cache reuse shader are proposed to perform such multi-rate shading across triangles and views. The results show that the newly proposed method outperforms the standard sort-middle shading and the state-of-the-art multi-rate shading by achieving considerably lower shading cost and memory bandwidth.

Multi-Criteria Land Evaluation of Suitability for the Sport of Foot Orienteering: A Case Study of Croatia and Slovenia

This paper describes a new multi-criteria land evaluation method, based on geomorphology and land cover, for the automated detection of suitable terrain for the sport of foot orienteering (footO). Reference data, in the form of areas already mapped and used for footO, was used to define criteria for geomorphology and land cover, and represents an expert knowledge component. The motivation for this research is that orienteering maps are often drawn for unfamiliar terrain that organizers of the event or mapmakers need to determine in advance, usually from base maps or by random reconnaissance. In a presented case study of Croatia and Slovenia, the geomorphology was derived from Digital Elevation Model over Europe (EU-DEM). The slope and aspect define components of the direction of the surface, and we tested the usability of these simple terrain parameters for the task. The CORINE dataset was used for the definition of the land cover. The results of the case study give potentially suitable areas for foot orienteering in Croatia and Slovenia, and in neighboring areas. Evaluation of the results, using reference areas as the control, proved that the proposed methodology gives a reliable indication of terrain suitability for orienteering. The method is simple, straightforward, and can be performed using standard GIS with common raster algorithms.

A GPU-Based Rasterization Algorithm for Boolean Operations on Polygons

A GPU-Based Rasterization Algorithm for Boolean Operations on Polygons

Semantic SLAM Based on Object Detection and Improved Octomap

Due to the development of the computer vision, machine learning, and deep learning technologies, the research community focuses not only on the traditional SLAM problems, such as geometric mapping and localization, but also on semantic SLAM. In this paper, we propose a Semantic SLAM system which builds the semantic maps with object-level entities, and it is integrated into the RGB-D SLAM framework. The system combines object detection module that is realized by the deep-learning method, and localization module with RGB-D SLAM seamlessly. In the proposed system, object detection module is used to perform object detection and recognition, and localization module is utilized to get the exact location of the camera. The two modules are integrated together to obtain the semantic maps of the environment. Furthermore, to improve the computational efficiency of the framework, an improved Octomap based on the Fast Line Rasterization Algorithm is constructed. Meanwhile, for the sake of accuracy and robustness of the semantic map, conditional random field is employed to do the optimization. Finally, we evaluate our Semantic SLAM through three different tasks, i.e., localization, object detection, and mapping. Specifically, the accuracy of localization and the mapping speed is evaluated on TUM data set. Compared with ORB-SLAM2 and original RGB-D SLAM, our system, respectively, got 72.9% and 91.2% improvements in dynamic environments localization evaluated by root-mean-square error. With the improved Octomap, the proposed Semantic SLAM is 66.5% faster than the original RGB-D SLAM. We also demonstrate the efficiency of object detection through quantitative evaluation in an automated inventory management task on a real-world data sets recorded over a realistic office.

A topology-preserving polygon rasterization algorithm

ABSTRACTConventional algorithms for polygon rasterization are typically designed to maintain non-topological characteristics. Consequently, topological relationships, such as the adjacency between polygons, may also be lost or altered, creating topological errors. This paper proposes a topology-preserving polygon rasterization algorithm to avoid topological errors. Four types of topological error may occur during polygon rasterization. The algorithm starts from an initial polygon rasterization and uses a set of preserving strategies to increase topological accuracy. The count of the four types of error measures the topological errors of the conversion. Topological accuracy is summarized as 1 minus the ratio of actual topological errors to the total number of possible error cases. When applied to a land-use dataset with a data volume of 128 MB, 127,836 polygons, and extending 1352 km2, the algorithm achieves a topological accuracy of more than 99% when raster cell size is 30 m or smaller (100% for 5 and 10 m). The effects of cell size, polygon shape, and number of iterations on topological accuracy are also examined.

Phenomenological Transparency.

Translucent objects such as fog, clouds, smoke, glass, ice, and liquids are pervasive in cinematic environments because they frame scenes in depth and create visually-compelling shots. Unfortunately, they are hard to render in real-time and have thus previously been rendered poorly compared to opaque surfaces. This paper introduces the first model for a real-time rasterization algorithm that can simultaneously approximate the following transparency phenomena: wavelength-varying ("colored") transmission, translucent colored shadows, caustics, volumetric light and shadowing, partial coverage, diffusion, and refraction. All render efficiently with order-independent draw calls and low bandwidth. We include source code.

Data decomposition method for parallel polygon rasterization considering load balancing

It is essential to adopt parallel computing technology to rapidly rasterize massive polygon data. In parallel rasterization, it is difficult to design an effective data decomposition method. Conventional methods ignore load balancing of polygon complexity in parallel rasterization and thus fail to achieve high parallel efficiency. In this paper, a novel data decomposition method based on polygon complexity (DMPC) is proposed. First, four factors that possibly affect the rasterization efficiency were investigated. Then, a metric represented by the boundary number and raster pixel number in the minimum bounding rectangle was developed to calculate the complexity of each polygon. Using this metric, polygons were rationally allocated according to the polygon complexity, and each process could achieve balanced loads of polygon complexity. To validate the efficiency of DMPC, it was used to parallelize different polygon rasterization algorithms and tested on different datasets. Experimental results showed that DMPC could effectively parallelize polygon rasterization algorithms. Furthermore, the implemented parallel algorithms with DMPC could achieve good speedup ratios of at least 15.69 and generally outperformed conventional decomposition methods in terms of parallel efficiency and load balancing. In addition, the results showed that DMPC exhibited consistently better performance for different spatial distributions of polygons.

A hierarchical network-based algorithm for multi-scale watershed delineation

Watershed delineation is a process for defining a land area that contributes surface water flow to a single outlet point. It is a commonly used in water resources analysis to define the domain in which hydrologic process calculations are applied. There has been a growing effort over the past decade to improve surface elevation measurements in the U.S., which has had a significant impact on the accuracy of hydrologic calculations. Traditional watershed processing on these elevation rasters, however, becomes more burdensome as data resolution increases. As a result, processing of these datasets can be troublesome on standard desktop computers. This challenge has resulted in numerous works that aim to provide high performance computing solutions to large data, high resolution data, or both. This work proposes an efficient watershed delineation algorithm for use in desktop computing environments that leverages existing data, U.S. Geological Survey (USGS) National Hydrography Dataset Plus (NHD+), and open source software tools to construct watershed boundaries. This approach makes use of U.S. national-level hydrography data that has been precomputed using raster processing algorithms coupled with quality control routines. Our approach uses carefully arranged data and mathematical graph theory to traverse river networks and identify catchment boundaries. We demonstrate this new watershed delineation technique, compare its accuracy with traditional algorithms that derive watershed solely from digital elevation models, and then extend our approach to address subwatershed delineation. Our findings suggest that the open-source hierarchical network-based delineation procedure presented in the work is a promising approach to watershed delineation that can be used summarize publicly available datasets for hydrologic model input pre-processing. Through our analysis, we explore the benefits of reusing the NHD+ datasets for watershed delineation, and find that the our technique offers greater flexibility and extendability than traditional raster algorithms.

래스터화 알고리즘을 위한 최적의 매니코어 프로세서 구조 탐색

래스터화 알고리즘을 위한 최적의 매니코어 프로세서 구조 탐색