Multi-Window Feature Map Fusion for Real-Time 3D Bounding Boxes
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Solution Overview
Problem
Autonomous vehicles face challenges in accurately drawing 3D bounding boxes in real-time driving environments due to insufficient semantic data from individual cameras and computationally intensive cross-relating of grid cells across feature maps, which hinders efficient object identification.
Innovation Solution
The approach involves grouping grid cells of feature maps based on object outlines and using windows or subsets of images/feature maps for self-attention, reducing computational demands and enhancing feature enrichment, thereby improving object identification and bounding box generation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If individual camera feature maps are used for object detection, then device complexity is reduced, but measurement precision of bounding boxes deteriorates due to insufficient semantic data
Solution Approach 1:
The patent combines feature maps from multiple cameras into a unified feature representation. The system integrates semantic data from different camera views to create enriched feature maps that provide sufficient information for accurate 3D bounding box generation, resolving the contradiction between system simplicity and detection precision.
Solution Approach 2:
The patent transitions from 2D feature maps to 3D spatial representations by incorporating depth information and spatial relationships across multiple camera views. This dimensional enhancement allows the system to generate accurate 3D bounding boxes while maintaining computational efficiency through structured feature fusion.
2Measurement precision
If cross-relating of all grid cells across feature maps is performed, then object identification accuracy is improved, but processing time increases due to computational intensity
Solution Approach 1:
The patent divides the feature maps into multiple windows or regions, processing only relevant grid cells within each window rather than all grid cells across entire feature maps. This segmentation reduces computational complexity while maintaining object identification accuracy by focusing processing on localized regions of interest.
Solution Approach 2:
The patent applies different processing strategies to different regions of feature maps based on their importance. High-priority regions containing potential objects receive detailed cross-relating processing, while low-priority background regions use simplified processing, optimizing the balance between accuracy and processing time.
3Measurement precision
If multiple cameras are integrated to enrich semantic data, then object detection accuracy is improved, but device complexity increases
Solution Approach 1:
The patent creates a unified feature map structure that can accommodate data from multiple camera types and configurations. This universal framework enables the system to integrate semantic data from various sources while maintaining a consistent processing pipeline, reducing the complexity increase that would otherwise result from multi-camera integration.
Data Source
AI summary
A perception system may be used to generate bounding boxes for objects in a vehicle scene. The perception system may receive images and feature maps corresponding to the received images. The perception system may generate multiple pluralities of windows and use the multiple pluralities of windows to enrich semantic data of the feature maps. The perception system may use the enriched semantic to generate one or more bounding boxes for objects in the vehicle scene.