Volumetric View Encoding for Bandwidth-Efficient AR/VR Rendering
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing rendering systems face significant computation and resource challenges due to the large size of volumetric image data, particularly in augmented and virtual reality applications, where efficient culling techniques are needed to reduce the amount of data transmitted and processed.
Innovation Solution
An encoder system segments volumetric image data into views based on visibility from specific viewpoints, applying view-frustum and occlusion culling to generate metadata, allowing a decoder system to render only necessary data with reduced computational and hardware resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If all volumetric image data is transmitted to the decoder system, then complete rendering accuracy is achieved, but network bandwidth and computational resources are excessively consumed
Solution Approach 1:
The encoder system segments volumetric image data into multiple views based on visibility from different viewpoints. Each view contains only the data visible from its corresponding viewpoint, dividing the complete volumetric dataset into multiple smaller, view-specific datasets that can be selectively transmitted and processed.
2Measurement precision
If all volumetric image data is transmitted to the decoder system, then complete rendering accuracy is achieved, but computational resources and processing time are excessively consumed
Solution Approach 1:
The encoder system extracts only the visible portions of volumetric image data from the complete dataset based on viewpoint analysis. By removing invisible data points that would not contribute to the final rendering, the system transmits a reduced dataset that maintains rendering accuracy while significantly decreasing computational load and processing time.
3Loss of energy
If view-based segmentation is implemented, then data transmission efficiency is improved, but system complexity increases due to metadata generation and viewpoint management
Solution Approach 1:
The encoder system performs preliminary viewpoint analysis and visibility determination before data encoding. By pre-calculating which data points are visible from each viewpoint and organizing the data accordingly, the system avoids the need for complex runtime processing at the decoder, shifting computational complexity to the encoding stage where it can be performed once during data preparation.
Data Source
AI summary
An encoding method comprises obtaining (101) an input set of volumetric image data, selecting (103) data from the image data for multiple views based on a visibility of the data from a respective viewpoint at a respective viewing direction and/or within a respective field of view such that a plurality of the views comprises only a part of the image data, encoding (105) each of the views as a separate output set (31), and generating (107) metadata which indicates the viewpoints. A decoding method comprises determining (121) a desired user viewpoint, obtaining (123) the metadata, selecting (125) one or more of the available viewpoints based on the desired user viewpoint, obtaining (127) one or more sets of image data in which one or more available views corresponding to the selected one or more available viewpoints have been encoded, and decoding (129) at least one of the one or more available views.