Adaptive Stereo Matching Optimization for Depth Estimation
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Solution Overview
Problem
Intelligent vehicles face challenges in accurately modeling the relationship between output quality and execution cycle in adaptive stereo matching, and optimizing system execution parameters under timing and energy constraints, leading to low depth estimation accuracy.
Innovation Solution
An adaptive stereo matching optimization method that acquires images from multiple perspectives, adjusts disparity value ranges using an adaptive stereo matching model, and employs Dynamic Voltage and Frequency Scaling (DVFS) and convolutional neural networks to optimize execution cycles and function parameters in real-time, while satisfying resource constraints such as energy, response time, and heat constraints.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If adaptive stereo matching is implemented to improve depth estimation accuracy, then measurement precision is improved, but device complexity increases due to the need for multiprocessor scheduling and resource constraint management
Solution Approach 1:
The patent dynamically adjusts execution parameters (voltage, frequency, execution cycle) of the stereo matching algorithm based on resource constraints and quality requirements. By changing operational parameters rather than system architecture, the patent achieves adaptive depth estimation accuracy while managing device complexity through software-controlled parameter optimization.
2Measurement precision
If extensive analysis on stereo matching is performed to improve model accuracy, then measurement precision is improved, but loss of time increases due to extended processing duration
Solution Approach 1:
The patent implements dynamic adjustment of the stereo matching execution cycle based on real-time resource constraints and quality requirements. Instead of performing fixed extensive analysis, the system adaptively determines the optimal execution duration, balancing model accuracy with processing time by dynamically scaling the analysis depth according to available resources.
Solution Approach 2:
The patent changes the execution cycle parameter of the stereo matching algorithm to optimize the balance between processing time and model accuracy. By adjusting the execution cycle based on resource constraints, the system achieves acceptable model accuracy without always requiring extensive analysis, thus reducing unnecessary processing time.
3Measurement precision
If adaptive stereo matching is implemented to improve depth estimation accuracy, then measurement precision is improved, but use of energy increases due to multiprocessor scheduling and real-time optimization
Solution Approach 1:
The patent dynamically adjusts voltage and frequency parameters of the processor based on resource constraints and quality requirements. By optimizing these electrical parameters, the system achieves improved depth estimation accuracy while minimizing energy consumption through adaptive power management rather than constant high-power operation.
Data Source
AI summary
The present disclosure provides an adaptive stereo matching optimization method, apparatus, and device, and a storage medium. The method includes: acquiring images of at least two perspectives of the same target scene, accordingly obtaining, through calculation, disparity value ranges corresponding to pixels in the target scene; and obtaining optimized depth value ranges by adjusting the disparity value ranges of the pixels in the target scene in real time through an adaptive stereo matching model; adjusting an execution cycle in the adaptive stereo matching model in real time through a DVFS algorithm according to a resource constraint condition of the processing system; and/or training on a plurality of scene image data sets through a convolutional neural network, so that the specific function parameters in the adaptive stereo matching model are correspondingly adjusted in real time according to the acquired different scene images.