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GTC ON-DEMAND

Presentation
Media
Abstract:

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.

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.

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Topics:
Autonomous Vehicles, Computer Vision, Real-Time Graphics
Type:
Talk
Event:
GTC Silicon Valley
Year:
2015
Session ID:
S5123
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Abstract:

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.

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.

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Topics:
Autonomous Vehicles, Artificial Intelligence and Deep Learning, Computer Vision, Mobile Applications
Type:
Talk
Event:
GTC Silicon Valley
Year:
2014
Session ID:
S4412
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Abstract:

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.

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.

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Topics:
Video & Image Processing, Medical Imaging & Radiology
Type:
Talk
Event:
GTC Silicon Valley
Year:
2013
Session ID:
S3193
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Abstract:

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).

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).

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Topics:
Developer - Algorithms
Type:
Talk
Event:
GTC Silicon Valley
Year:
2012
Session ID:
S2096
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Speakers:
Gernot Ziegler
- NVIDIA
 
Topics:
Programming Languages
Type:
Talk
Event:
ISC
Year:
2011
Session ID:
ISC1101
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Abstract:

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.

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.

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Topics:
Developer - Algorithms, Medical Imaging & Radiology, Video & Image Processing
Type:
Talk
Event:
GTC Silicon Valley
Year:
2010
Session ID:
2020
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Speakers:
Allan Rasmusson, Gernot Ziegler
- University of Aarhus, NVIDIA
Abstract:
Explore a new technique in the detection of common regions in a 2D/3D data array. Connected components along the axes are linked before actual label propagation starts. The algorithm is completely gather-based, which allows for several optimizations in the CUDA C implementation. It enables real-time frame rates for the analysis of typical 2D images and interactive frame rates for the analysis of typical volume data.
Explore a new technique in the detection of common regions in a 2D/3D data array. Connected components along the axes are linked before actual label propagation starts. The algorithm is completely gather-based, which allows for several optimizations in the CUDA C implementation. It enables real-time frame rates for the analysis of typical 2D images and interactive frame rates for the analysis of typical volume data.  Back
 
Topics:
Developer - Algorithms, Computer Vision, Medical Imaging & Radiology, Video & Image Processing
Type:
Talk
Event:
GTC Silicon Valley
Year:
2010
Session ID:
2021
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