3D Posture Estimation via 2D Image Coordinate Transformation
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Solution Overview
Problem
Current obstacle detection technologies in autonomous vehicles rely on two-dimensional image analysis, which lacks three-dimensional information, limiting their applicability in various driving scenarios, as they cannot effectively map two-dimensional detection results to three-dimensional spaces for real-time obstacle avoidance.
Innovation Solution
A method and apparatus that estimate the three-dimensional posture of objects by obtaining two-dimensional posture information and three-dimensional size information, determining key point coordinates in an object coordinate system, and establishing a transformation relationship between camera and object coordinate systems using geometrical correspondence and algorithms like PnP, enabling the mapping of two-dimensional obstacle detection to a three-dimensional space.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If two-dimensional image analysis is used for obstacle detection, then the detection process is simple and fast, but the three-dimensional information is insufficient for comprehensive driving scenario coverage
Solution Approach 1:
The patent transforms two-dimensional image data into three-dimensional spatial information by establishing coordinate transformation relationships between camera coordinate system and vehicle coordinate system. This allows the system to obtain depth, distance, and spatial position information from 2D images, effectively adding a dimensional aspect without requiring complex 3D sensing hardware.
Solution Approach 2:
The patent introduces an intermediary computational model that acts as a bridge between 2D image data and 3D spatial representation. By using coordinate transformation matrices and geometric relationships as intermediaries, the system can derive three-dimensional obstacle information from two-dimensional detections, avoiding the need for direct 3D sensing while preserving spatial accuracy.
2Loss of information
If three-dimensional obstacle detection is implemented using traditional methods, then comprehensive spatial information is obtained, but the system complexity and cost increase significantly
Solution Approach 1:
The patent creates a virtual three-dimensional representation of obstacles by computationally transforming 2D detection results. Instead of using physical 3D sensors like laser radars, the system copies spatial information into a digital 3D model through coordinate transformations, achieving 3D detection capability with 2D imaging hardware.
Solution Approach 2:
The patent replaces complex mechanical 3D sensing systems (such as laser radars and multiple camera arrays) with a computational approach using 2D images and mathematical transformations. This substitution of mechanical complexity with algorithmic processing significantly reduces system complexity while maintaining 3D detection capability.
3Measurement precision
If coordinate transformation and geometrical relationship calculations are performed, then accurate three-dimensional posture estimation is achieved, but the computational processing time increases
Solution Approach 1:
The patent pre-establishes coordinate transformation matrices and geometric relationship models during system initialization or offline calibration phases. By preparing these computational frameworks in advance, the system minimizes real-time calculation requirements during actual obstacle detection, thus maintaining high precision while reducing processing time for critical real-time operations.
Data Source
AI summary
The present disclosure provides a three-dimensional posture estimating method and apparatus, a device and a computer storage medium, wherein the method comprises: obtaining two-dimensional posture information of an object in an image and three-dimensional size information of the object; determining coordinates of key points of the object in an object coordinate system according to the three-dimensional size information of the object; determining a transformation relationship between a camera coordinate system and the object coordinate system according to a geometrical relationship between coordinates of key points of the object in the object coordinate system and the two-dimensional posture information of the object. Application of this manner to the field of autonomous driving may implement mapping a detection result of a two-dimensional obstacle to a three-dimensional space to obtain its posture.