Vehicle Surround-View 3D Modeling for Multi-Camera Distortion Correction
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Solution Overview
Problem
Existing vehicle control systems face challenges in accurately projecting and providing a three-dimensional (3D) representation of the surrounding environment due to image distortion when using multiple cameras, which affects stability and user experience, particularly during parking scenarios.
Innovation Solution
A vehicle control apparatus and method that utilizes a processor to obtain depth and semantic information from multiple cameras, convert these into space tensors in a reference coordinate system, correct and cluster the tensors, and generate 3D modeling information using algorithms like mesh generation to accurately represent the vehicle's surroundings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Area of stationary object
If multiple cameras are used to capture surrounding environment, then the coverage area and information completeness are improved, but image distortion increases
Solution Approach 1:
The patent transforms 2D images from multiple cameras into 3D space tensors, adding a spatial dimension to the data representation. This dimensional transformation allows the system to model and correct distortions by representing objects in three-dimensional space with coordinates (x, y, z) and physical quantities (depth, width, height), thereby resolving the distortion issue while maintaining multi-camera coverage
Solution Approach 2:
The patent changes the parameter representation from standard 2D image coordinates to 3D space tensor parameters including depth information, object dimensions, and spatial coordinates. By introducing depth as an additional parameter and transforming the coordinate system, the patent corrects image distortion while preserving the comprehensive coverage provided by multiple cameras
2Measurement precision
If image distortion is reduced through delicate manipulation, then image accuracy is improved, but system complexity increases
Solution Approach 1:
The patent replaces complex mechanical image correction mechanisms with a computational approach using space tensors and coordinate transformations. Instead of physically adjusting cameras or using complex optical systems, the patent uses algorithms to transform 2D images into 3D space tensors and back, achieving accurate distortion correction through software-based coordinate system transformations
Solution Approach 2:
The patent introduces space tensors as an intermediary representation between 2D camera images and 3D environmental models. These space tensors serve as a mediator that contains both 2D image information and 3D spatial characteristics, allowing for accurate distortion correction through mathematical transformations without requiring complex mechanical adjustment systems
3Measurement precision
If 3D space tensors are generated and corrected, then distance measurement accuracy is improved, but processing time increases
Solution Approach 1:
The patent performs preliminary transformation of 2D images into 3D space tensors in advance, organizing the data with depth and spatial information pre-calculated. This preliminary action allows for faster real-time processing during actual operation, as the complex transformation work is done beforehand, reducing processing time while maintaining accurate distance measurement capabilities
Data Source
AI summary
A vehicle control apparatus may include cameras and a processor. The processor may: obtain, based on first images obtained through the cameras, depth information indicating a distance between a vehicle and an external object and semantic information including data associated with a type of the external object; generate sets of first space tensors corresponding to the first images representing at least a portion within a specified radius from the vehicle; convert the sets of first space tensors to sets of second space tensors represented in a reference coordinate system; identify an object space tensor regarding a first specified type in the sets of second space tensors based on identifying a reference image; generate sets of third space tensors by correcting the sets of second space tensors based on a set of reference space tensors included in the reference image; and output modeling information representing the first images in 3D space.