2020 |
Ioannidis, Charalabos; Boutsi, Argyro-Maria MULTITHREADED RENDERING FOR CROSS-PLATFORM 3D VISUALIZATION BASED ON VULKAN API Journal Article In: ISPRS - Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci., XLIV-4/W1- , pp. 57–62, 2020, ISSN: 2194-9034. Abstract | Links | BibTeX | Tags: 3D visualization, Computer graphics, geospatial data, graphics API, Vulkan @article{Ioannidis2020a,Abstract. The visualization of large-sized 3D geospatial models is a graphics intensive task. With ever increasing size and complexity, more computing resources are needed to attain speed and visual quality. Exploiting the parallelism and the multi-core performance of the Graphics Processing Unit (GPU), a cross-platform 3D viewer is developed based on the Vulkan API and modern C++. The proposed prototype aims at the visualization of a textured 3D mesh of the Cultural Heritage by enabling a multi-threaded rendering pipeline. The rendering workload is distributed across many CPU threads by recording multiple command buffers in parallel and coordinating the host and the GPU rendering phases. To ensure efficient multi-threading behavior and a minimum overhead, synchronization primitives are exploiting for ordering the execution of queues and command buffers. Furthermore, push-constants are used to send uniform data to the GPU and render passes to adapt to the tile-based rendering of the mobile devices. The proposed methodology and technical solution are designed, implemented and tested for Windows, MacOS and Android on Vulkan-compatible GPU hardware by compiling the same codebase. The benchmarking on multiple hardware, architectures and platforms explores the performance improvement for the different approaches compared to one-thread and showcase the potential of the 3D viewer to handle large datasets at no expense of visual quality and geometric fidelity in the absence of high-end technological resources. |
2020 |
MULTITHREADED RENDERING FOR CROSS-PLATFORM 3D VISUALIZATION BASED ON VULKAN API Journal Article In: ISPRS - Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci., XLIV-4/W1- , pp. 57–62, 2020, ISSN: 2194-9034. |