Structured Light Depth Estimation for Autonomous Vehicles
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Solution Overview
Problem
Existing methods for depth estimation in computer vision, particularly in autonomous vehicles, face challenges in accuracy and cost-effectiveness, especially when using monocular images or stereo images.
Innovation Solution
The use of structured light techniques to generate ground truth depth values, which are then used to improve the accuracy of depth estimation in computer vision systems, particularly in autonomous vehicles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If LIDAR-based approaches are used for depth estimation, then measurement precision is improved, but device complexity and cost increase
Solution Approach 1:
The patent uses a camera to capture images that serve as a copy or representation of the scene, and processes these image copies through structured light analysis to derive depth information. This avoids the need for expensive LIDAR hardware while achieving depth estimation through computational processing of optical information.
Solution Approach 2:
The patent replaces the mechanical LIDAR system (which uses physical laser scanning and time-of-flight measurements) with an optical-computational system using cameras and structured light pattern analysis. This substitution transitions from active mechanical ranging to passive optical capture with computational processing.
2Measurement precision
If LIDAR-based approaches are used for depth estimation, then measurement precision is improved, but cost increases
Solution Approach 1:
The patent employs standard camera sensors and projected light patterns that are significantly cheaper than LIDAR systems. The structured light patterns are generated computationally or through simple projection, and the depth information is extracted through image processing algorithms, replacing expensive specialized hardware with affordable consumer-grade components.
Solution Approach 2:
The system uses standard camera images as inexpensive copies of the scene, processing these through structured light analysis to extract depth information. This approach avoids the high cost of LIDAR hardware while achieving comparable depth estimation functionality through computational methods.
3Device complexity
If monocular or stereo images are used for depth estimation, then device complexity is reduced, but measurement precision deteriorates
Solution Approach 1:
The patent applies structured light patterns to the scene before capturing the image. This preliminary action of projecting known light patterns onto objects provides additional geometric information that can be analyzed in the captured image, enabling accurate depth estimation from a single or stereo camera without requiring complex active sensing hardware.
Solution Approach 2:
The patent changes the parameters of the light field by projecting structured light patterns with specific spatial frequencies and orientations. By analyzing how these known patterns deform when reflected from objects, the system can extract depth information that would otherwise be unavailable from standard monocular or stereo imaging, effectively transforming the problem from ambiguous 2D image interpretation to constrained 3D reconstruction.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach provides a cost-effective method for generating accurate depth maps, improving the accuracy of depth estimation for monocular and stereo images, and enabling more precise object detection and navigation in autonomous systems.
Implementation Method 1
The projected light reflects off at least one object in the scene
Data Source
AI summary
A depth map for a scene may be generated by projecting, by a light projector, an illumination pattern onto a scene; capturing, by a camera, a camera image of the scene; generating, by a computing device, a plurality of ground truth depth values for sample pixels of the camera image based at least in part on the illumination pattern; and estimating a depth map for the scene based at least in part on the camera image and the ground truth depth values for sample pixels.