3D Scene Reconstruction Using Dual Complementary Pattern Illumination
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Solution Overview
Problem
Current methods for 3D scene reconstruction using structured illumination face challenges such as high latency in image acquisition and computation, and uncertainty in determining point-to-point correspondence, which hinder efficient and accurate three-dimensional reconstruction.
Innovation Solution
The use of dual complementary pattern illumination, where a first reference image with a full grid of dots is projected onto a scene, followed by a second reference image with randomly dropped dots, allows for low-latency and high-accuracy correspondence determination through feature vector generation, enabling rapid and precise 3D scene reconstruction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple images are taken with structured illumination patterns to determine point-to-point correspondence, then 3D scene reconstruction accuracy is improved, but image acquisition latency increases
Solution Approach 1:
The illumination pattern is segmented into two complementary subsets (first and second patterns) that together form the complete structured illumination pattern. Each subset contains a portion of the total patterns, allowing the system to capture corresponding images from both subsets and combine them to achieve full 3D reconstruction accuracy while reducing the number of captures needed compared to traditional single-pattern approaches
Solution Approach 2:
The system performs preliminary organization of illumination patterns into complementary subsets before image capture. By pre-defining which patterns belong to the first subset and which belong to the second subset, the system eliminates the need for complex real-time pattern selection and correspondence matching, thereby reducing computation latency while maintaining reconstruction accuracy
2Measurement precision
If multiple images are taken with structured illumination patterns to determine point-to-point correspondence, then 3D scene reconstruction accuracy is improved, but computation latency increases
Solution Approach 1:
The correspondence determination computation is segmented into two separate processes: one for the first pattern subset and one for the second pattern subset. Each subset is processed independently to determine its corresponding points, and the results are combined. This segmentation allows for more efficient computation compared to processing all patterns simultaneously, reducing overall computation latency while maintaining full correspondence accuracy
Solution Approach 2:
The system uses complementary pattern subsets that act as simplified copies of the complete illumination pattern. Each subset contains enough information to establish correspondence for its portion of the scene, allowing the system to perform multiple simpler correspondence determinations rather than one complex determination, thereby reducing computation time
3Area of stationary object
If traditional structured illumination patterns are used, then complete scene coverage is achieved, but correspondence determination uncertainty increases
Solution Approach 1:
The complete illumination pattern is segmented into two complementary subsets, where each subset covers the entire scene but with different pattern configurations. By capturing images from both subsets and combining the correspondence information, the system achieves complete scene coverage while reducing uncertainty through the redundancy and complementary nature of the two pattern sets
Solution Approach 2:
Each pattern subset is designed with local quality variations that complement the other subset. The first subset and second subset have different local pattern characteristics that, when combined, provide robust correspondence determination across the entire scene. This local differentiation reduces ambiguity in matching points between reference and captured images
Data Source
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AI summary
An apparatus, system and process for utilizing dual complementary pattern illumination of a scene when performing depth reconstruction of the scene are described. The method may include projecting a first reference image and a complementary second reference image on a scene, and capturing first image data and second image data including the first reference image and the complementary second reference image on the scene. The method may also include identifying features of the first reference image from features of the complementary second reference image. Furthermore, the method may include performing three dimensional (3D) scene reconstruction for image data captured by the imaging device based on the identified features in the first reference image.