Road Surface 3D Shape Estimation From Bird's-Eye View Distortion
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing road surface shape estimation methods, such as Structure from Motion (SfM), require significant computational resources and time, and struggle to accurately estimate road surface shapes due to the need for multiple viewpoints, leading to inefficiencies in road defect detection and maintenance.
Innovation Solution
A road surface shape estimation device that compares birds-eye views of images captured from different positions to identify corresponding portions and calculates the amount of movement between these portions, utilizing image distortion caused by uneven road surfaces to estimate the three-dimensional shape accurately.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If Structure from Motion (SfM) method is used to estimate road surface shape, then three-dimensional model can be built, but a large amount of calculation is required and high accuracy estimation is difficult
Solution Approach 1:
The patent extracts only the essential information needed for road surface shape estimation by converting images to birds-eye views and comparing specific corresponding portions between images. This eliminates the need for comprehensive three-dimensional model building required by SfM, significantly reducing calculation complexity while maintaining estimation accuracy.
Solution Approach 2:
The patent divides the road surface into multiple portions and processes each portion separately by identifying corresponding regions between birds-eye views. This segmentation approach simplifies the overall calculation by breaking down the complex three-dimensional reconstruction problem into smaller, more manageable comparisons of local image regions.
2Measurement precision
If Structure from Motion (SfM) method is used to estimate road surface shape, then three-dimensional model can be built, but a long period is required for estimation
Solution Approach 1:
The patent extracts only the essential distortion information from birds-eye view images that directly relates to road surface unevenness, eliminating the time-consuming processes of comprehensive three-dimensional model building and multiple viewpoint acquisitions required by SfM methods.
Solution Approach 2:
The patent performs birds-eye view conversion as a preliminary step that simplifies subsequent comparisons. By pre-processing images into birds-eye views and identifying corresponding portions beforehand, the actual shape estimation requires minimal computation time compared to performing full three-dimensional reconstruction from scratch.
3Reliability
If multiple viewpoints are used to capture road surface images, then more comprehensive data is obtained, but the requirement for a large number of viewpoints increases complexity and time requirements
Solution Approach 1:
The patent extracts the essential geometric information needed for shape estimation by converting images to birds-eye views and comparing corresponding portions. This approach obtains sufficient data comprehensiveness from just two viewpoints, eliminating the need for the large number of viewpoints required by traditional SfM methods.
Solution Approach 2:
The patent uses a minimal set of two viewpoints, which is less than the extensive multi-viewpoint requirement of SfM, yet achieves sufficient reliability for road surface shape estimation by focusing on extracting and comparing specific distortion features from the birds-eye view images.
Data Source
AI summary
A road surface shape estimation device includes an identification unit that compares a first birds-eye view obtained by converting a first image of a road surface, captured from a first position, into a birds-eye view with a second birds-eye view obtained by converting a second image of the road surface, captured from a second position different from the first position, into a birds-eye view and identifies, for each of a plurality of portions of the road surface, a first portion in the first birds-eye view corresponding to the portion and a second portion in the second birds-eye view corresponding to the portion and an estimation unit that calculates an amount of movement between the first portion and the second portion over the images and estimates an uneven shape of the road surface on the basis of the amount of movement.