Surround View Augmentation via 3D Object Model Substitution
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Solution Overview
Problem
Current automated driving systems face limitations in object detection and classification due to narrow field-of-view and innate system constraints, particularly in the near-field of the vehicle, which affects accuracy and application.
Innovation Solution
The implementation of networked on-body vehicle cameras with object recognition and model substitution using Natural Surround Vision techniques, which employ image segmentation and depth inference to replace detected objects with 3D models, enhancing situational awareness and providing virtual perspectives.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If networked on-body vehicle cameras with object recognition and model substitution are implemented, then target acquisition accuracy and classification accuracy are improved, but device complexity increases
Solution Approach 1:
The patent creates virtual copies of detected objects by substituting them with 3D models retrieved from a database. Instead of directly processing complex real-world objects, the system replaces them with standardized 3D representations that are easier to analyze and classify, thereby improving measurement precision while managing device complexity through model substitution
Solution Approach 2:
The patent introduces an intermediary processing layer that includes image segmentation, depth inference, and object recognition modules. This intermediary layer processes raw camera data before final classification, acting as a mediator that transforms complex visual data into structured information that can be more accurately analyzed, thus improving target acquisition accuracy without directly increasing the complexity of core detection systems
2Measurement precision
If networked on-body vehicle cameras with object recognition and model substitution are implemented, then classification accuracy is improved, but device complexity increases
Solution Approach 1:
The system substitutes detected objects with standardized 3D models from a database, replacing complex real-world variations with controlled virtual representations. This copying approach enables more accurate classification by comparing against known models rather than dealing with the full complexity of real objects, thereby improving classification accuracy while managing device complexity
Solution Approach 2:
The patent divides the object recognition process into segmented stages: initial detection, image segmentation, depth inference, model retrieval, and classification. By segmenting the complex classification task into manageable stages, the system improves classification accuracy through systematic processing while avoiding the need for a single overly complex classification device
3Loss of information
If Natural Surround Vision techniques with image segmentation and depth inference are used, then situational awareness is enhanced, but processing requirements and device complexity increase
Solution Approach 1:
The patent applies image segmentation to divide complex scenes into distinct object regions, and depth inference to estimate three-dimensional structure from two-dimensional images. These segmentation techniques break down complex visual information into manageable components, enhancing situational awareness by recovering lost spatial information while managing processing requirements through structured data organization
Solution Approach 2:
The system creates virtual 3D copies of detected objects and scenes, substituting complex real-world data with simplified virtual representations that preserve essential spatial and structural information. This copying approach enhances situational awareness by reconstructing three-dimensional space from limited camera views while reducing the complexity of processing actual real-world data
Data Source
AI summary
Presented are intelligent vehicle systems with networked on-body vehicle cameras with camera-view augmentation capabilities, methods for making/using such systems, and vehicles equipped with such systems. A method for operating a motor vehicle includes a system controller receiving, from a network of vehicle-mounted cameras, camera image data containing a target object from a perspective of one or more cameras. The controller analyzes the camera image to identify characteristics of the target object and classify these characteristics to a corresponding model collection set associated with the type of target object. The controller then identifies a 3D object model assigned to the model collection set associated with the target object type. A new “virtual” image is generated by replacing the target object with the 3D object model positioned in a new orientation. The controller commands a resident vehicle system to execute a control operation using the new image.