Learned Stereo Architecture with Dynamic Disparity Ranges
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing stereo depth estimation systems are limited by predefined baselines, require extensive real-world data collection and labeling, and lack flexibility in adapting to different stereo camera systems, leading to high training costs and inefficiencies.
Innovation Solution
A learned stereo architecture utilizing fully differentiable 3D convolutions and trained with fully synthetic labeled data, enabling flexibility across various stereo camera systems and allowing dynamic adjustments to range and resolution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If stereo depth estimation systems use image-based sensors only, then system versatility and sensor package requirements improve, but depth sensing accuracy and reliability deteriorate
Solution Approach 1:
The system dynamically adjusts the disparity range parameter to adapt to different depth sensing requirements. By changing the parameter that defines the disparity search range, the system can optimize performance for different scenarios while maintaining accurate depth estimation using only image-based sensors
Solution Approach 2:
The learned stereo architecture implements dynamic adjustments to the disparity range based on input conditions. The system can modify its operational parameters in real-time, allowing it to adapt to varying depth requirements and maintain reliability across different environments without additional sensors
2Loss of time
If learned stereo architecture is trained with fully synthetic labeled data, then training cost and data collection time improve, but measurement precision and reliability of depth estimation deteriorate
Solution Approach 1:
The system uses synthetic rendered images as copies or simulations of real-world scenes for training. These synthetic images with known ground truth disparity values serve as training data, eliminating the need for expensive and time-consuming real-world data collection while still enabling the network to learn accurate depth estimation patterns
Solution Approach 2:
The system performs preliminary rendering of synthetic training data in advance, creating a comprehensive training dataset through computer graphics simulations before deployment. This preliminary action of generating synthetic training pairs with known disparities prepares the model for accurate real-world performance without requiring real-world data collection
3Productivity
If learned stereo architecture is designed for a predefined range of disparities, then computational efficiency and processing speed improve, but adaptability to different stereo systems and ranges deteriorates
Solution Approach 1:
The learned stereo architecture is designed with universal adaptability to work across multiple stereo camera systems with different baselines and disparity ranges. The network can be configured with different disparity range parameters to suit various hardware configurations, making it a multi-functional solution that maintains processing efficiency while adapting to diverse systems
Solution Approach 2:
The system dynamically adjusts the disparity range parameter based on the specific stereo system configuration and application requirements. This dynamic reconfigurability allows the same base architecture to efficiently process different disparity ranges without requiring multiple specialized models, balancing speed and adaptability
Data Source
AI summary
A method for controlling a learned stereo architecture includes implementing a learned stereo architecture capable of generating refined disparity estimates for a predefined range of disparities based on training with fully synthetic image data; inputting a first range parameter into the learned stereo architecture, where the first range parameter is a subset of the predefined range of disparities; receiving, from a first stereo system having a first baseline, a first stereo image pair; generating a first disparity estimate, wherein disparities estimated in the first disparity estimate correspond to the first range parameter; upsampling the first disparity estimate to a resolution corresponding to a resolution of the first stereo image pair to form a first full resolution disparity estimate; refining the first full resolution disparity estimate with a first disparity residual thereby generating a first refined full resolution disparity estimate; and outputting the first refined full resolution disparity estimate.