Single Camera 3D Depth Estimation Using Disparity Mapping
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Stereo camera systems face challenges in determining depth measurements, particularly for points within homogeneous intensity or color regions, leading to unreliable depth estimates for a subset of visible points, and require multiple cameras to generate a single three-dimensional image.
Innovation Solution
A method is developed to determine discrete layers of a scene using continuous disparity mapping, allowing for the approximation of a target object's distance and projecting pixels onto a virtual plane, utilizing sensors like gyroscopes and GPS to account for camera movement and improve depth calculation accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If stereo camera systems use multiple image sensors to determine depth, then three-dimensional information can be captured, but reliable depth estimates can only be obtained for a subset of visible points, particularly failing for points in homogeneous intensity or color regions
Solution Approach 1:
The patent introduces an intermediary processing step between capturing stereo images and determining depth. A depth map is first generated from the stereo image pair, then this depth map is used to guide super-resolution processing. The depth information acts as a mediator that enables subsequent enhancement of image quality while preserving the three-dimensional structure, allowing depth estimation to be extended to regions where direct stereo matching fails.
Solution Approach 2:
The patent performs preliminary depth estimation using stereo triangulation before attempting to resolve correspondence problems in homogeneous regions. By first establishing depth values for visible points where correspondence can be solved, and then using these preliminary results to guide further processing in difficult regions, the system builds upon initial successes to achieve more complete coverage.
2Measurement precision
If stereo camera systems use multiple cameras to generate a single three-dimensional image, then depth information can be obtained, but the system complexity increases
Solution Approach 1:
The patent combines multiple processing functions into a unified pipeline. Super-resolution enhancement, depth map generation, and three-dimensional image reconstruction are merged into an integrated process where outputs from one stage become inputs to the next. This combining of functions allows the system to achieve enhanced three-dimensional imaging while managing complexity through coordinated processing rather than separate independent systems.
Solution Approach 2:
The patent creates a multi-functional processing system that handles both image enhancement and three-dimensional reconstruction. The same processed images and depth maps are used for multiple purposes: generating enhanced two-dimensional views and constructing three-dimensional representations. This multi-functionality reduces the need for separate dedicated systems for each task.
3Productivity
If conventional stereo triangulation is used for range imaging, then depth estimates can be obtained for some points, but the correspondence problem remains unsolved for regions of homogeneous intensity or color
Solution Approach 1:
The patent implements feedback by using the generated depth map to guide subsequent super-resolution processing. The depth information feeds back into the image enhancement process, allowing the system to adapt its processing based on the three-dimensional structure. This feedback loop enables the system to maintain consistency between depth estimates and enhanced image details, improving reliability in regions where direct matching is difficult.
Solution Approach 2:
The patent transitions from two-dimensional image matching to three-dimensional processing by incorporating depth as an additional dimension. Instead of attempting to solve the correspondence problem solely in the image plane, the system uses depth information to guide processing in three-dimensional space, allowing points to be identified and processed based on their spatial position rather than relying only on two-dimensional intensity patterns.
Data Source
AI summary
Two dimensional images captured by a camera or other device may be used to generate three dimensional information for target objects included in the two dimensional images. Sensor information and other information associated with the device capturing the two dimensional images may be obtained and used to determine a displacement or movement of the camera during capture of the two dimensional images. The displacement or movement may be used to calculate a distance of the target object in the two dimensional images. The distance information may be used to generate virtual planes corresponding to the target objects.