Multi-Camera Spatial Feature Fusion for 3D Object Cuboid Prediction
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Solution Overview
Problem
Modern image-based machine learning systems for autonomous vehicles face challenges in associating predictions from multiple cameras with overlapping fields of view, leading to issues like predicting multiple objects for a single object and leveraging different perspectives effectively.
Innovation Solution
The system captures images from multiple cameras with overlapping fields of view, generates spatial feature maps using CNNs or position-sensitive transformer networks, identifies overlapping portions, combines these maps to define predicted cuboids, and uses these cuboids for autonomous vehicle control.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Area of stationary object
If multiple cameras with overlapping fields of view are used to capture images, then the coverage area and detection capability are improved, but the complexity of associating predictions from different cameras increases
Solution Approach 1:
The patent introduces spatial feature maps as an intermediary representation that bridges multiple camera views. These feature maps encode spatial relationships and object characteristics in a unified format, enabling the system to associate predictions from different cameras without directly comparing raw predictions. The spatial feature maps serve as a mediator that resolves the complexity of multi-camera prediction association.
Solution Approach 2:
The patent generates separate spatial feature maps for each camera view, creating copies of the scene representation tailored to each camera's perspective. These copied representations are then integrated to form a comprehensive understanding of the environment, allowing the system to leverage multiple views while maintaining organized, view-specific processing.
2Measurement precision
If spatial feature maps from multiple cameras are combined, then the accuracy of object detection is improved, but the computational complexity increases
Solution Approach 1:
The patent segments the feature extraction and combination process into distinct stages: individual spatial feature map generation for each camera, identification of overlapping portions, and combination of these portions. This segmentation allows the system to process information in manageable chunks rather than attempting to combine all camera data simultaneously, reducing peak computational demands while maintaining detection accuracy.
Solution Approach 2:
The patent combines only the overlapping portions of spatial feature maps rather than processing entire maps from all cameras. This partial action approach focuses computational resources on the regions where multiple cameras provide information, improving detection accuracy in critical areas while avoiding the excessive computational cost of processing all camera data in full detail.
3Reliability
If overlapping portions of spatial feature maps are identified and combined, then the reliability of prediction association is improved, but the processing time increases
Solution Approach 1:
The patent performs preliminary identification of overlapping portions in spatial feature maps before combining them. By pre-identifying which regions contain information from multiple cameras, the system prepares the data structure in advance, enabling more efficient combination operations. This preliminary action reduces the time required for the actual association process while maintaining reliable prediction integration.
Data Source
AI summary
Systems and methods for image-based perception. The methods comprise: capturing images by a plurality of cameras with overlapping fields of view; generating, by a computing device, spatial feature maps indicating locations of features in the images; identifying, by the computing device, overlapping portions of the spatial feature maps; generating, by the computing device, at least one combined spatial feature map by combining the overlapping portions of the spatial feature maps together; and/or using, by the computing device, the at least one combined spatial feature map to define a predicted cuboid for at least one object in the images.