Point Cloud View-Position Angle Processing for 6DoF VR
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Solution Overview
Problem
Current virtual reality streaming technologies limit user experience to a panorama view, restricting efficient streaming of additional dimensions such as front/back, up/down, and left/right views, and angles, which hinders immersive 6DoF media experiences.
Innovation Solution
A method and apparatus that acquire volumetric data, convert it to point cloud data, project and encode it into 2D images, and compose a media file with metadata indicating 6DoF positions and angles, allowing for independent encoding and transmission of partitions, enabling efficient 360-degree virtual reality streaming.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If volumetric data is converted to point cloud data and projected onto 2D images for streaming, then 360-degree virtual reality experiences are enabled, but data complexity and processing requirements increase
Solution Approach 1:
The point cloud data is divided into multiple partitions, each representing different spatial regions or viewing angles. This segmentation allows the system to process and stream only relevant portions of the 3D scene based on user position and orientation, reducing overall processing complexity while maintaining 6DoF capability
Solution Approach 2:
The patent projects 3D point cloud data onto 2D image planes, effectively reducing dimensionality for streaming purposes. By encoding multiple 2D projections that can be reconstructed into 3D views, the system enables complex 6DoF experiences while transmitting simplified 2D data streams
2Productivity
If point cloud data is partitioned and encoded independently, then streaming efficiency is improved, but encoding complexity increases
Solution Approach 1:
The point cloud is divided into multiple independent partitions that can be encoded separately. Each partition can be processed in parallel, improving streaming efficiency and allowing selective transmission based on user needs, while the modular encoding approach manages complexity through division of labor
Solution Approach 2:
The point cloud data is pre-partitioned and organized into structured regions before encoding begins. This preliminary organization enables more efficient encoding by establishing spatial relationships and data structures in advance, reducing the computational burden during actual encoding while maintaining independence of partitions
Data Source
AI summary
There is includes a method and apparatus comprising computer code configured to cause a processor or processors to perform acquiring volumetric data of at least one visual three-dimensional scene, converting the volumetric data to point cloud data, projecting the point cloud data onto two-dimensional images, encoding the point cloud data projected onto the two-dimensional images, and composing a media file encapsulating both metadata and the encoded point cloud data, where the metadata indicates a six-degrees-of-freedom media.