Sharding VR Video Data for Parallel Rendering
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Virtual reality content generation systems face challenges in processing abundant data from multiple cameras, often resulting in insufficient detail or less than 360-degree environments when using fewer cameras to reduce data processing burdens.
Innovation Solution
A method involving sharding of raw virtual reality video data into segments, assigning each shard to worker nodes for processing, and reassigning failed shards to ensure complete data processing, while concatenating video and audio renders to generate immersive 360-degree content.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a smaller number of cameras is used to reduce data processing burdens, then data processing complexity is reduced, but virtual reality content quality deteriorates with insufficient detail and less than 360-degree environments
Solution Approach 1:
The patent divides the processing of virtual reality data into shards, where each shard contains data from a subset of cameras. Multiple worker nodes process different shards in parallel, enabling the system to handle data from many cameras (maintaining high content quality) without overwhelming any single processing unit (managing complexity).
Solution Approach 2:
The patent introduces a distributed processing dimension by deploying multiple worker nodes across different computing resources. This transforms the single-point processing bottleneck into a multi-point parallel processing system, allowing the handling of abundant camera data while maintaining manageable complexity at each node.
2Manufacturing precision
If multiple camera modules are used to capture comprehensive 360-degree environments, then virtual reality content quality is improved, but data processing burden increases
Solution Approach 1:
The patent segments the camera array into groups, with each group's data forming a shard assigned to a specific worker node. This segmentation allows the system to process data from multiple cameras (improving content quality) by distributing the workload across multiple nodes (managing complexity).
Solution Approach 2:
The patent performs preliminary sharding of camera data before processing, organizing data into manageable chunks and assigning them to worker nodes in advance. This preliminary organization enables parallel processing of data from multiple cameras without creating processing bottlenecks.
3Device complexity
If raw virtual reality video data is processed without sharding, then processing workflow is simpler, but processing efficiency deteriorates due to the abundance of data
Solution Approach 1:
The patent segments the large volume of raw virtual reality video data into smaller shards, each containing data from a subset of cameras and time segments. This segmentation enables parallel processing across multiple worker nodes, dramatically improving processing efficiency while maintaining manageable workflow complexity at each node.
Solution Approach 2:
The patent introduces parallel processing as an additional dimension to the workflow, transforming sequential processing of abundant data into concurrent processing across multiple worker nodes. This dimensional change from single-threaded to multi-threaded processing significantly boosts efficiency.
4Productivity
If data is processed in larger segments, then fewer processing operations are needed, but processing reliability deteriorates when failures occur
Solution Approach 1:
The patent segments data into smaller shards processed by different worker nodes, which increases the number of processing operations but improves reliability through distribution. If one worker node fails, other nodes continue processing their assigned shards, ensuring processing reliability.
Solution Approach 2:
The patent implements local error handling and recovery at each worker node level, where failed shards can be reassigned to different worker nodes. This localized approach to error management maintains overall processing reliability without requiring complete system restarts.
Data Source
AI summary
A method includes defining first, second, and third shards of raw three-dimensional video data in a state file, wherein each shard includes raw video feeds; assigning each shard to a corresponding worker node in a set of worker nodes; processing the shards at the set of worker nodes to generate one or more three-dimensional video renders for each shard; determining, from the state file, that processing of the first shard is complete, processing of the second shard is complete, and processing of the third shard is incomplete; and generating three-dimensional content by concatenating a first three-dimensional video render, a first audio render associated with the first shard, a second three-dimensional video render, and a second audio render associated with the second shard, and a filler video that is a placeholder for a third three-dimensional video render until the third shard is processed.