Predictive VR Display System with Post-Rendering Correction
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Solution Overview
Problem
Existing virtual reality systems face challenges in achieving low-latency rendering due to high computational demands, increased complexity of 3D models, higher display resolutions, and limited processor capacity in mobile devices, leading to noticeable lag between user movements and virtual environment updates.
Innovation Solution
A predictive virtual reality display system that uses pose prediction to render images based on a user's future pose and applies efficient rerendering approximations, such as pixel translation, to reduce latency, while also employing post-rendering corrections and variable resolution rendering to improve synchronization and user experience.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If high resolution rendering is used to improve image quality, then manufacturing precision is improved, but loss of time increases due to longer rendering latency
Solution Approach 1:
The patent divides the display area into multiple regions with different resolutions. The central region (fovea) is rendered at high resolution while peripheral regions are rendered at lower resolutions. This segmentation allows the system to reduce overall rendering time and latency while maintaining high visual quality in the most important viewing area.
Solution Approach 2:
The patent applies different rendering qualities to different parts of the display. High resolution rendering is applied only to the central foveal region where human vision is most acute, while peripheral regions use lower resolution. This local quality approach optimizes the balance between visual fidelity and rendering performance.
2Manufacturing precision
If complex 3D models are used to improve virtual environment realism, then manufacturing precision is improved, but productivity decreases due to slower rendering speeds
Solution Approach 1:
The patent segments the virtual environment rendering by applying different levels of detail to different spatial regions. Complex 3D models are rendered in full detail only in the central foveal region, while peripheral regions use simplified models or lower detail representations. This allows realistic virtual environments to be maintained where needed while improving overall rendering speed.
Solution Approach 2:
The patent implements local quality by varying the complexity of 3D model rendering across different display regions. High-fidelity complex models are rendered only in the central viewing area where visual acuity is highest, while peripheral areas use less computationally intensive representations, thereby maintaining realism where it matters most while improving rendering productivity.
3Loss of time
If high processor capacity is used to improve rendering performance, then power consumption increases, but loss of time decreases due to faster rendering
Solution Approach 1:
The patent segments the rendering workload by resolution region, allocating high processor capacity only to the central foveal region while using reduced processing for peripheral regions. This segmentation enables the system to achieve acceptable rendering latency without requiring maximum processor power consumption across the entire display.
Solution Approach 2:
The patent applies local quality by concentrating computational resources on the central region where high rendering performance is most critical for user experience, while reducing processor power consumption in peripheral regions where lower rendering quality is imperceptible or less noticeable to users.
Data Source
AI summary
A virtual reality display system that generates display images in two phases: the first phase renders images based on a predicted pose at the time the display will be updated; the second phase re-predicts the pose using recent sensor data, and corrects the images based on changes since the initial prediction. The second phase may be delayed so that it occurs just in time for a display update cycle, to ensure that sensor data is as accurate as possible for the revised pose prediction. Pose prediction may extrapolate sensor data by integrating differential equations of motion. It may incorporate biomechanical models of the user, which may be learned by prompting the user to perform specific movements. Pose prediction may take into account a user's tendency to look towards regions of interest. Multiple parallel pose predictions may be made to reflect uncertainty in the user's movement.