Multi-Camera Vision Field Computing for High-Speed Dynamic Scenes
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Solution Overview
Problem
Conventional camera systems with high frame rates are expensive and limited, leading to motion blur in captured images of dynamic scenes when motion velocity exceeds the frame rate, hindering effective perception and capture of high-speed events.
Innovation Solution
A method for vision field computing using a multi-camera system with spatial and temporal interleaved sampling, silhouette back projection, and temporal decoupling to reconstruct a dynamic scene geometry model with higher resolution than conventional cameras, enabling effective perception and capture of high-speed events without expensive industrial-grade cameras.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional cameras with high frame rate are used to capture high-speed dynamic scenes, then motion blur is reduced, but the system cost becomes extremely high
Solution Approach 1:
The patent divides the capture task among multiple conventional cameras (at least three) positioned at different spatial locations, each capturing a portion of the dynamic scene. By segmenting the temporal sampling across multiple spatial positions, the system achieves high-speed capture capability without requiring each individual camera to operate at extremely high frame rates, thus reducing overall system cost while maintaining effective temporal resolution
Solution Approach 2:
The patent transitions from a single-camera temporal sampling approach to a multi-camera spatial-temporal sampling approach. By adding the spatial dimension with multiple cameras positioned at different locations, the system can reconstruct high-speed dynamic scenes using conventional cameras, effectively converting a temporal resolution problem into a spatial-temporal sampling problem that can be solved with lower-cost hardware
2Ease of manufacture
If conventional cameras with limited frame rate are used, then system cost is reduced, but motion blur occurs when motion velocity exceeds frame rate
Solution Approach 1:
The patent merges the data from multiple conventional cameras capturing the same dynamic scene from different spatial positions. By combining these partial observations through spatial-temporal sampling and reconstruction algorithms, the system achieves effective high-speed capture capability that exceeds what any single conventional camera could achieve alone, thereby improving temporal resolution without increasing individual camera specifications or system cost
3Measurement precision
If feature matching algorithms are used for 3D reconstruction, then reconstruction accuracy is improved, but the method fails when motion blur is present
Solution Approach 1:
The patent changes the fundamental parameters and approach of the reconstruction algorithm from feature-based matching to spatial-temporal sampling-based reconstruction. Instead of relying on feature detection and matching which fails with motion blur, the system uses the geometric relationships and temporal sampling patterns from multiple camera positions to reconstruct the dynamic scene, making the method applicable to high-speed scenes where feature matching would fail
Data Source
AI summary
A method for vision field computing may comprise the following steps of: forming a sampling system for a multi-view dynamic scene; controlling cameras in the sampling system for the multi-view dynamic scene to perform spatial interleaved sampling, temporal interleaved exposure sampling and exposure-variant sampling; performing spatial intersection to the sampling information in the view subspace of the dynamic scene and temporal intersection to the sampling information in the time subspace of the dynamic scene to reconstruct a dynamic scene geometry model; performing silhouette back projection based on the dynamic scene geometry model to obtain silhouette motion constraints for the view angles of the cameras; performing temporal decoupling for motion de-blurring with the silhouette motion constraints; and reconstructing a dynamic scene 3D model with a resolution larger than nominal resolution of each camera by a 3D reconstructing algorithm.