Risk Map Prioritized 3D Rendering for Bandwidth Constraints
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Solution Overview
Problem
The high resource requirements for rendering and streaming large volumes of data points in three-dimensional environments exceed the capabilities of user devices, leading to inefficiencies in data transmission and display, particularly when rendering movement or different scenes within these environments.
Innovation Solution
The implementation of risk maps that predict changes in a three-dimensional environment allows for prioritized rendering and streaming by identifying pixels most likely to change, enabling out-of-order rendering and streaming of image data, ensuring that data packets for the most visually differentiated areas are transmitted first, even under bandwidth constraints.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the entire point cloud is rendered at high resolution, then the visual quality and detail of the 3D environment is improved, but the computational resources and rendering time required exceed the capabilities of user devices
Solution Approach 1:
The patent segments the point cloud into multiple regions based on risk maps that predict changes in the 3D environment. Instead of rendering the entire point cloud uniformly, the system divides it into high-risk areas (likely to change) and low-risk areas (less likely to change), then applies different rendering strategies to each segment. This allows high visual quality where needed while reducing computational resources where changes are unlikely.
Solution Approach 2:
The patent applies local quality by rendering different parts of the point cloud with different levels of detail based on their risk profiles. High-risk regions receive high-resolution rendering to maintain visual quality, while low-risk regions can be rendered at lower resolution or updated less frequently. This localized approach maintains measurement precision where critical while reducing overall computational burden.
2Reliability
If all data points are rendered and streamed for every scene change, then the completeness of the 3D environment is maintained, but the data transmission time exceeds the display refresh rate
Solution Approach 1:
The patent performs preliminary action by using risk maps to predict which areas of the 3D environment are most likely to change before actually rendering and streaming the data. The system pre-identifies high-risk regions that need to be updated, allowing the rendering and transmission processes to focus only on those areas rather than processing the entire environment, thus maintaining reliability while improving speed.
Solution Approach 2:
The patent extracts only the necessary data points and regions that are most likely to change based on risk map predictions. Instead of transmitting the complete point cloud for every scene change, the system extracts and transmits only the high-risk regions that will actually change, maintaining environmental completeness where needed while dramatically reducing transmission time and bandwidth requirements.
3Device complexity
If the rendering system processes all data points in order, then the processing sequence is simple, but the display cannot keep up with the rendering rate due to large data volumes
Solution Approach 1:
The patent introduces dynamics by changing the rendering processing sequence from a static sequential order to a dynamic risk-based order. The system uses risk maps to dynamically determine which data points should be rendered and processed first, allowing the rendering system to adapt its processing sequence based on predicted changes. This dynamic approach enables the display to keep up with rendering by prioritizing areas that will actually change, thereby improving frame rate while managing complexity through risk-based decision making.
Data Source
AI summary
A system prioritizes the rendering and streaming of image data based on risk maps that predict change in a three-dimensional (“3D”) environment. The system receives primitives that are distributed across a 3D space to represent the 3D environment. The system generates a first image based on primitives that fall within a first view frustum, and generates a risk map with a risk value for each particular pixel of the first image. Each risk value quantifies a probability that a pixel of the first image associated with that risk value changes as a result of changing the first view frustum to a second view frustum. The system then performs an out-of-order rendering of primitives that fall within the second view frustum based on the risk value for each first image pixel that is replaced in a second image with a rendered primitive from the second view frustum.