Layered Depth Image Rendering for VR Performance
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Solution Overview
Problem
In virtual reality (VR) environments, the rendering of complex geometric scenes using Image-Based Rendering (IBR) techniques can be computationally intensive, leading to increased rendering times due to the large number of pixels and data required, which can overwhelm the processing capabilities of Graphics Processing Units (GPUs) and result in suboptimal performance for real-time rendering with six degrees of freedom (6DOF).
Innovation Solution
A method involving a computer-implemented process that identifies and groups pixel samples in a Layered Depth Image (LDI) representation based on an error metric, creating texture maps and blocks of data, which are then used to optimize rendering by reducing the number of individual pixels processed, utilizing a GPU to rasterize and apply texture maps efficiently, thereby minimizing total partitioning error and improving rendering performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If Image-Based Rendering (IBR) techniques are used to render geometrically complex scenes, then rendering quality is improved, but rendering time increases
Solution Approach 1:
The patent segments the scene representation into multiple Layered Depth Image (LDI) tiles, where each tile contains a subset of pixel samples. This segmentation allows the GPU to process and render smaller manageable blocks independently, reducing the overall rendering time while maintaining rendering quality through selective processing of high-error regions
Solution Approach 2:
The patent applies local quality by computing error metrics for each pixel sample and selectively processing only those samples with high error values. This allows the system to focus computational resources on regions that most impact rendering quality, rather than uniformly processing all pixels, thus reducing total rendering time while maintaining quality where it matters most
2Measurement precision
If the number of pixel samples is increased to improve scene representation accuracy, then rendering accuracy is improved, but computational load on GPU increases
Solution Approach 1:
The patent implements partial action by processing only a subset of pixel samples - specifically those with the highest error metrics - rather than processing all pixel samples uniformly. This selective processing reduces computational load on the GPU while maintaining scene representation accuracy by focusing on the most critical samples that contribute most to rendering quality
Solution Approach 2:
The patent changes the parameter of pixel sample selection from uniform processing to error-metric-based selective processing. By introducing error metrics as a selection criterion and dynamically choosing which samples to process based on their error values, the system reduces computational load while preserving accuracy in regions that need it most
3Manufacturing precision
If all pixel samples are processed individually, then rendering precision is maintained, but processing efficiency decreases
Solution Approach 1:
The patent merges multiple pixel samples into grouped blocks organized as LDIs and further divides them into manageable tiles. By combining samples into structured blocks and processing them as unified units rather than individual pixels, the system improves processing efficiency through batch operations while maintaining rendering precision through the error-metric-based selective processing of high-priority samples
Data Source
AI summary
In one general aspect, a computer-implemented method can include identifying a plurality of pixel samples included in a layered depth image (LDI) representation of a scene for rendering in a three-dimensional (3D) image in a virtual reality (VR) space, grouping, by a processor, a subset of the plurality of pixel samples into a block of data, including extracting each pixel sample included in the subset of the plurality of pixel samples from the LDI representation of the scene for inclusion in the block of data based on an error metric associated with the respective pixel sample, creating, by the processor, a texture map for a block of data, the texture map being associated with the block of data, storing the block of data and the texture map, and triggering a rendering of the 3D image in the VR space using the block of data and the texture map.