Monocular Depth Estimation Parameter Tuning for Vehicle Distance Mapping
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Solution Overview
Problem
Conventional monocular depth estimation (MDE) systems in vehicles lack the ability to accurately measure distances and provide enhanced views, particularly in scenarios like backing up or parking, due to measurement-less depth maps and insufficient sensitivity of sensors, leading to reliance on driver interpretation.
Innovation Solution
The use of optimal image analysis parameters to adjust and emphasize points of interest and de-emphasize irrelevant road segments in depth maps, enhancing the accuracy of depth estimation by converting pixel values to real-world distances, and utilizing cloud or edge servers to share these parameters for improved vehicle navigation and parking assistance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional monocular depth estimation systems are used, then the system complexity remains low, but the measurement precision of depth maps is insufficient
Solution Approach 1:
The system changes parameters by introducing optimal image analysis parameters that are determined based on scene characteristics, vehicle state, and sensor conditions. These parameters are used to adjust the depth map generation process, converting pixel values to real-world distances with improved accuracy without requiring complex multi-sensor setups.
Solution Approach 2:
The patent introduces an intermediary processing layer that includes scene understanding components and parameter determination modules. This intermediary layer processes the raw image and depth map data, applies optimal parameters, and generates enhanced depth information, acting as a mediator between the simple camera input and the improved depth estimation output.
2Reliability
If standard depth map generation is used, then the processing speed remains fast, but the ability to highlight critical objects and provide enhanced views is insufficient
Solution Approach 1:
The system applies local quality by adjusting different regions of the depth map differently based on their importance. Critical objects and areas are highlighted with enhanced visualization and accurate real-world distance conversion, while less important regions use standard processing, creating a depth map with varying quality levels matched to their significance.
Solution Approach 2:
The system performs preliminary scene understanding and parameter determination before final depth map generation. By pre-identifying critical objects, road segments, and optimal parameters based on scene characteristics, the system prepares the necessary adjustments in advance, enabling reliable enhanced views without adding significant processing complexity during real-time operation.
3Loss of information
If monocular depth estimation is used without parameter adjustment, then the ease of operation remains high, but the loss of information regarding real-world distances is significant
Solution Approach 1:
The system performs preliminary determination of optimal image analysis parameters based on scene characteristics, vehicle state, and sensor operational parameters. This pre-processing step establishes the correct scaling and conversion factors needed to accurately translate monocular pixel values into real-world distances, preventing information loss before the main depth estimation process.
Solution Approach 2:
The system changes the parameter set by introducing scene-specific optimal parameters that compensate for the inherent limitations of monocular depth estimation. These parameters include scaling factors, offset values, and conversion coefficients that are determined based on camera characteristics, focal length, and scene geometry, enabling accurate real-world distance recovery from single-image pixel data.
Data Source
AI summary
Systems and methods are provided to determine optimal (image analysis) parameters for generating or adapting/adjusting a monocular depth estimation (MDE) depth map based on various factors, such as vehicle operating conditions, environmental conditions, road conditions, etc. After determining the optimal parameters to use, the optimal parameters can be stored on a cloud server or an edge server along with the vehicle location for use when another vehicle approaches the location to perform MDE without needing to independently determine the optimal parameters to use. Moreover, the use of such optimal image analysis parameters, enable a richer/enhanced view of the environment/area around or about a vehicle, where image analysis parameters may be used to more accurately interpret or otherwise characterize a depth map.