In the first part of the talk we'll present the VK_NVX_device_generated_commands extension, which allows the GPU to generate the most frequent rendering commands on its own, including pipeline changes.In the second part well introduce new Vulkan VR extensions, discuss their usage and provide short samples for common use cases. We also give a brief update on the new OpenGL VR SLI extension, plans for new API interoperability and shortly touch on new Vulkan Nsight functionality.
OpenGL provides new features for accelerating scenes with many objects, which are typically found in professional visualization markets. This talk will provide details on the usage of the features and their effect on real-life models. Furthermore we will showcase how more work for rendering a scene can be off-loaded to the GPU, such as efficient occlusion culling or matrix calculations.
The goal of this session is to demonstrate techniques that improve GPU scalability when rendering complex scenes. This is achieved through a modular design that separates the scene graph representation from the rendering backend. We will explain how the modules in this pipeline are designed and give insights to implementation details, which leverage GPU's compute capabilities for scene graph processing. Our modules cover topics such as shader generation for improved parameter management, synchronizing updates between scenegraph and rendering backend, as well as efficient data structures inside the renderer.
To analyze datasets visually, systems with fast feedback loops on user interaction are beneficial. In this session rendering and preprocessing techniques for medical volume data will be presented using OpenGL and CUDA. In the context of the coronary artery disease the analysis of individual vessel branches is important. We show how local transfer function application and generation by means of histogramm analysis can help navigating and finding details in the datasets. Furthermore, domain-specific acceleration and illustration techniques for volume rendering are also applied to datasets from brain aneurysms.