Multi-Camera Feature Synchronization via Transformer Timing
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Solution Overview
Problem
Existing systems fail to synchronize the timing of multiple camera inputs effectively, leading to desynchronization of features in images captured by different cameras, which hinders accurate depth estimation and other visual perception tasks in applications like autonomous vehicles.
Innovation Solution
A method that combines timing information with the machine learning system to synchronize features across multiple camera inputs using transformer-based layers, enabling cross-view attention and alignment of features in time, even when cameras capture images at different times due to varying exposure settings and parasitic effects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple cameras capture images independently without time synchronization, then each camera can operate autonomously with simple processing, but the features from different cameras become desynchronized leading to inaccurate visual perception
Solution Approach 1:
The patent introduces timing information as an intermediary element that bridges the gap between multiple camera inputs. By combining timing data with image features through transformer layers, the system achieves temporal alignment without requiring complex hardware synchronization mechanisms, thus improving depth estimation accuracy while maintaining reasonable system complexity
Solution Approach 2:
The patent replaces traditional mechanical or hardware-based time synchronization methods with a computational approach using transformer-based machine learning models. Instead of physically synchronizing camera triggers or using complex timing hardware, the system uses software-based feature alignment through attention mechanisms, reducing device complexity while achieving precise temporal synchronization
2Adaptability or versatility
If cameras use varying exposure settings to adapt to different lighting conditions, then each camera can optimize its capture quality, but timing differences and parasitic effects cause feature desynchronization
Solution Approach 1:
The patent handles varying exposure settings and timing differences by transforming the feature representation through transformer layers that incorporate timing information as additional parameters. This allows the system to accommodate different exposure conditions and parasitic effects without compromising feature synchronization reliability, maintaining adaptability while ensuring reliable temporal alignment
Solution Approach 2:
The transformer-based model uses attention mechanisms that effectively provide feedback about temporal relationships between camera inputs. By analyzing the timing information and adjusting feature representations accordingly, the system maintains reliable synchronization even when cameras operate with varying exposure settings and experience parasitic effects
Data Source
AI summary
An apparatus, method and computer-readable media are disclosed for processing images. For example, a method is provided for processing images for one or more visual perception tasks using a machine learning system including one or more transformer layers. The method includes: obtaining a plurality of input images associated with a plurality of spatial views of a scene; generating, using a machine learning-based encoder of a machine learning system, a plurality of features from the plurality of input images; and combining timing information associated with capture of the plurality of input images with at least one input of the machine learning system to synchronize the plurality of features in time.