Real-Time 3D Depth Profile Generation Using Epipolar Geometry
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Solution Overview
Problem
Current 3D imaging systems are unable to generate 3D images or depth profiles in real time, requiring post-processing and large memory and processing power, making them unsuitable for applications like parking sensors and lacking in providing full depth profiles.
Innovation Solution
A system using a moveable video camera and processor to continuously capture and process 2D image data, calculating depth profiles in real time by determining pixel similarity along an epipolar line, with a display showing calculated depth and confidence levels, allowing for real-time generation of 3D images or depth profiles without regularization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current 3D imaging systems process data after acquisition, then memory capacity and processing power can be increased, but real-time image generation is not achieved
Solution Approach 1:
The system performs preliminary actions by continuously capturing 2D image frames and pre-processing them to identify corresponding pixels and calculate epipolar lines during the data acquisition phase itself. This allows depth calculations to be prepared in advance, enabling real-time 3D image generation without requiring extensive post-processing, thus resolving the contradiction between measurement precision and time loss.
2Measurement precision
If current 3D systems use large memory capacity and processing power, then depth profile accuracy can be improved, but system complexity and cost increase
Solution Approach 1:
The patent replaces complex mechanical/computational 3D imaging systems with a simplified approach based on optical correlation and epipolar geometry. Instead of using large arrays of sensors and complex processing hardware, the system uses standard video cameras with software-based depth calculation algorithms that leverage the geometric constraints of epipolar lines, significantly reducing device complexity while maintaining depth profile accuracy.
Solution Approach 2:
The system changes the computational parameters by working in image space rather than object space, and by utilizing the epipolar constraint to reduce the search dimensionality from 2D to 1D. This parameter transformation allows accurate depth calculation with minimal processing power and memory, resolving the contradiction between measurement precision and device complexity.
3Device complexity
If SLAM systems are used to generate 3D data, then processing requirements are reduced, but full depth profiles cannot be produced
Solution Approach 1:
The system applies partial action by focusing computational resources only on calculating depth along epipolar lines rather than performing full 3D reconstruction. By using the epipolar constraint, the system processes only the necessary pixel correspondences required for depth calculation, achieving full depth profiles with reduced processing requirements compared to complete SLAM approaches.
Data Source
AI summary
A system generating a 3D depth profile of an object, including: a moveable video camera to continually capture 2D image data of the object as the camera moves, to capture plural 2D image frames each including plural pixels; a processor configured to: receive the 2D image data; determine position of the camera when each frame is captured; calculate depth of part of an object shown in a pixel in a first frame with respect to a reference, by identifying the part in at least one further frame and calculating the depth using camera positions of the first image and at least one further image; and determine depth profile of the object, for plural parts of the object shown in the pixels. A display displays the depth profile and indicates in real time, as data is gathered, the parts of the object for which the depth profile has been calculated.