GPU UTILIZATION IN GEOPROCESSING BIG GEODATA: A REVIEW

The expansion of data collection from remote sensing and other geographic data sources, as well as from other technology such as cloud, sensors, mobile, and social media, have made mapping and analysis more complex. Some geospatial applications continue to rely on conventional geospatial processing, where limitation on computation capabilities often lacking to attain significant data interpretation. In recent years, GPU processing has improved far more GIS applications than using CPU alone. As a result, numerous researchers have begun utilising GPUs for scientific, geometric, and database computations in addition to graphics hardware use. This paper summarizes parallel processing concept and architecture, the development of GPU geoprocessing for big geodata ranging from remote sensing and 3D modelling to smart cities studies. This paper also addresses the GPU future trends advancement opportunities with other technologies, machine learning, deep learning, and cloud-based computing.

Size-Adaptive Texture Atlas Generation and Remapping for 3D Urban Building Models

A high-fidelity 3D urban building model requires large quantities of detailed textures, which can be non-tiled or tiled ones. The fast loading and rendering of these models remain challenges in web-based large-scale 3D city visualization. The traditional texture atlas methods compress all the textures of a model into one atlas, which needs extra blank space, and the size of the atlas is uncontrollable. This paper introduces a size-adaptive texture atlas method that can pack all the textures of a model without losing accuracy and increasing extra storage space. Our method includes two major steps: texture atlas generation and texture atlas remapping. First, all the textures of a model are classified into non-tiled and tiled ones. The maximum supported size of the texture is acquired from the graphics hardware card, and all the textures are packed into one or more atlases. Then, the texture atlases are remapped onto the geometric meshes. For the triangle with the original non-tiled texture, new texture coordinates in the texture atlases can be calculated directly. However, as for the triangle with the original tiled texture, it is clipped into many unit triangles to apply texture mapping. Although the method increases the mesh vertex number, the increased geometric vertices have much less impact on the rendering efficiency compared with the method of increasing the texture space. The experiment results show that our method can significantly improve building model rendering efficiency for large-scale 3D city visualization.

Real-Time Image Based Lighting for 360 Degree Panoramic Video

<p>The application of the newly popular content medium of 360 degree panoramic video to the widely used offline lighting technique of image based lighting is explored, and a system solution for real-time image based lighting of virtual objects using only the provided 360 degree video for lighting is developed. The system solution is suitable for use on live streaming video input, and is shown to run on consumer grade graphics hardware at the high resolutions and framerates necessary for comfortable viewing on head mounted displays, rendering at over 60 frames per second for stereo output at 1182x1464 per eye on a mid-range graphics card. Its use in several real-world applications is also studied, and extension to consider real-time shadowing and reflection is explored.</p>

Real-Time Image Based Lighting for 360 Degree Panoramic Video

<p>The application of the newly popular content medium of 360 degree panoramic video to the widely used offline lighting technique of image based lighting is explored, and a system solution for real-time image based lighting of virtual objects using only the provided 360 degree video for lighting is developed. The system solution is suitable for use on live streaming video input, and is shown to run on consumer grade graphics hardware at the high resolutions and framerates necessary for comfortable viewing on head mounted displays, rendering at over 60 frames per second for stereo output at 1182x1464 per eye on a mid-range graphics card. Its use in several real-world applications is also studied, and extension to consider real-time shadowing and reflection is explored.</p>

ReS2tAC—UAV-Borne Real-Time SGM Stereo Optimized for Embedded ARM and CUDA Devices

With the emergence of low-cost robotic systems, such as *UAV, the importance of embedded high-performance image processing has increased. For a long time, FPGAs were the only processing hardware that were capable of high-performance computing, while at the same time preserving a low power consumption, essential for embedded systems. However, the recently increasing availability of embedded GPU-based systems, such as the NVIDIA Jetson series, comprised of an ARM CPU and a NVIDIA Tegra GPU, allows for massively parallel embedded computing on graphics hardware. With this in mind, we propose an approach for real-time embedded stereo processing on ARM and CUDA-enabled devices, which is based on the popular and widely used Semi-Global Matching algorithm. In this, we propose an optimization of the algorithm for embedded CUDA GPUs, by using massively parallel computing, as well as using the NEON intrinsics to optimize the algorithm for vectorized SIMD processing on embedded ARM CPUs. We have evaluated our approach with different configurations on two public stereo benchmark datasets to demonstrate that they can reach an error rate as low as 3.3%. Furthermore, our experiments show that the fastest configuration of our approach reaches up to 46 FPS on VGA image resolution. Finally, in a use-case specific qualitative evaluation, we have evaluated the power consumption of our approach and deployed it on the DJI Manifold 2-G attached to a DJI Matrix 210v2 RTK *UAV, demonstrating its suitability for real-time stereo processing onboard a *UAV.

Vis3D+ A tightly integrated GPU-accelerated computation and rendering framework for interactive 3D image visualization

This thesis presents extensions to an interactive 3D image visualization framework. The existing software framework provides functionality for interactively visualizing 3D medical data. The extensions consist of software modules that execute directly on the graphics hardware, utilizing the massively parallel, general-purpose computing platform provided by modern graphics processing units (GPUs). These GPUbased software modules are designed to support the execution of volume image processing algorithms, implemented using recently available GPU programs known as “compute shaders”, as well as to support interactive editing of the algorithms’ output. The new modules are seamlessly integrated as new stages in a GPU-based rendering pipeline provided by the existing framework. In this thesis, an example volume image processing algorithm known as level set segmentation is implemented and demonstrated. In addition, a new editing module is demonstrated that enables user modification of this algorithm’s output by extending a pre-existing volume “painting” interface.

Vis3D+ A tightly integrated GPU-accelerated computation and rendering framework for interactive 3D image visualization

This thesis presents extensions to an interactive 3D image visualization framework. The existing software framework provides functionality for interactively visualizing 3D medical data. The extensions consist of software modules that execute directly on the graphics hardware, utilizing the massively parallel, general-purpose computing platform provided by modern graphics processing units (GPUs). These GPUbased software modules are designed to support the execution of volume image processing algorithms, implemented using recently available GPU programs known as “compute shaders”, as well as to support interactive editing of the algorithms’ output. The new modules are seamlessly integrated as new stages in a GPU-based rendering pipeline provided by the existing framework. In this thesis, an example volume image processing algorithm known as level set segmentation is implemented and demonstrated. In addition, a new editing module is demonstrated that enables user modification of this algorithm’s output by extending a pre-existing volume “painting” interface.

GPU accelerated voxel-based machining simulation

The simulation of subtractive manufacturing processes has a long history in engineering. Corresponding predictions are utilized for planning, validation and optimization, e.g., of CNC-machining processes. With the up-rise of flexible robotic machining and the advancements of computational and algorithmic capability, the simulation of the coupled machine-process behaviour for complex machining processes and large workpieces is within reach. These simulations require fast material removal predictions and analysis with high spatial resolution for multi-axis operations. Within this contribution, we propose to leverage voxel-based concepts introduced in the computer graphics industry to accelerate material removal simulations. Corresponding schemes are well suited for massive parallelization. By leveraging the computational power offered by modern graphics hardware, the computational performance of high spatial accuracy volumetric voxel-based algorithms is further improved. They now allow for very fast and accurate volume removal simulation and analysis of machining processes. Within this paper, a detailed description of the data structures and algorithms is provided along a detailed benchmark for common machining operations.