Image Processing for Segmented 3D Tracking in Volumetric Video
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Solution Overview
Problem
Existing tracking technologies for 3D models face exponential calculation increases with the number of vertices, leading to processing capacity limitations, especially in volumetric video applications.
Innovation Solution
The image processing apparatus divides tracking processing for each object in a frame group, utilizing correspondence relation information to identify and track 3D models, and outputs metadata for each object, allowing for high-speed processing and alignment of track start frames.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If tracking processing is conducted on all vertices of multiple 3D models simultaneously, then comprehensive tracking coverage is achieved, but calculation complexity increases exponentially
Solution Approach 1:
The patent segments the tracking processing by dividing vertices into multiple groups and assigning different track start frames to each group. This segmentation allows the tracking algorithm to process vertices in smaller batches rather than all at once, reducing the exponential calculation complexity while maintaining comprehensive tracking coverage across all vertices through multiple tracking groups.
2Measurement precision
If tracking processing is performed on a large number of objects, then complete object tracking is achieved, but processing speed decreases
Solution Approach 1:
The patent applies preliminary action by pre-assigning different track start frames to different vertex groups before the actual tracking processing begins. This preparation allows the tracking algorithm to start processing multiple objects simultaneously from different temporal points, improving processing speed while ensuring complete tracking coverage through the coordinated use of multiple tracking groups.
3Adaptability or versatility
If traditional tracking processing is used without frame alignment, then processing flexibility is maintained, but data management efficiency decreases
Solution Approach 1:
The patent changes the parameter of track start frames by systematically assigning different start frame values to different vertex groups. This parameter change enables synchronized alignment of tracking data across multiple objects, significantly improving data management efficiency and enabling better integration with standardized formats like MPEG-DASH, while maintaining processing flexibility through configurable group assignments.
Data Source
AI summary
High-speed tracking processing on a large number of objects is achieved. Time-series shape data composed of a frame group in which each of frames contains 3D models representing three-dimensional shapes of a plurality of objects, respectively, is obtained. Then, tracking processing is conducted for each object based on correspondence relation information contained in the obtained time-series shape data.