Learn how the GPU's real-time graphics capabilities can be used to interactively visualize and enhance the camera system of modern cars. The GPU simplifies design, interactive calibration and testing of the car's computer vision systems, and even allows for creating simulated environments where the behavior of the car's computer vision can be tested to pass standard safety tests or navigational street situations.
Discover how mobile GPUs enable modern features of car driving in a power-efficient and standardized way, by providing the fundamental building blocks of computer vision to the higher-level reasoning functions that enable the car to detect lanes, park automatically, avoid obstacles, etc. We explain the challenges of having to fit into a given time budget, and how the low-level machine vision such as corner detection, feature tracking and even more advanced functionality such as 3D surrounding reconstruction is achieved in the context of the car's systems and its outside environment.
Locating connected regions in images and volumes is a substantial building block in image and volume processing pipelines. We demonstrate how the Connected Components problem strongly benefits from a new feature in the Kepler architecture, direct thread data exchange through the SHUFFLE instruction.
In this presentation, we show how ripmaps can replace Summed Area Tables (SATs) for the purpose of computing a large number of spatially varying box filter kernels throughout the input data, providing both higher accuracy and higher speed for typical use cases. For this purpose, we demonstrate an implementation of ripmap generation in CUDA C (accelerated by shared memory usage), and a texture-cache based box filter for spatially varying kernel sizes, which can be implemented in both CUDA C and graphics-based APIs (e.g. OpenGL and DirectX).
Learn how to accelerate marching cubes on the GPU by taking advantage of the GPU's high memory bandwidth and fast on-chip shared memory in a data expansion algorithm that can extract the complete iso-surface mesh from (dynamic) volume data without requiring any data transfers back to the CPU.