Structured Light 3D Reconstruction Handling Global Illumination
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Solution Overview
Problem
Conventional structured light methods fail to accurately measure 3D shapes in scenes with global illumination effects like inter-reflections, sub-surface scattering, and volumetric scattering due to systematic errors, as they assume direct illumination only, leading to significant performance degradation and errors in real-world applications.
Innovation Solution
The method employs multiple sets of structured illumination patterns resilient to global light transport errors, which are designed to handle different modes of light transport and optical artifacts, allowing for error detection and correction without explicit separation of direct and global illumination components, using spatial frequencies and logical coding techniques.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional structured light methods are used to measure 3D shapes, then acquisition time can be reduced and depth resolution can be increased, but systematic errors occur in scenes with global illumination effects
Solution Approach 1:
The patent segments the illumination into multiple distinct patterns (e.g., alternating light/dark stripes, different spatial frequencies) that are projected sequentially onto the scene. By analyzing the response to each segmented pattern, the system can distinguish between direct illumination and global illumination components, thereby resolving systematic errors in depth measurement while maintaining high depth resolution.
2Reliability
If methods remove or reduce global light transport effects through polarization or high-frequency modulation, then direct component separation is achieved, but hardware complexity and acquisition time increase significantly
Solution Approach 1:
The patent replaces complex mechanical hardware systems (polarization filters, moving mirrors, specialized optics) with a computational approach using standard projectors and cameras. Multiple illumination patterns are projected sequentially and processed through algorithms that mathematically separate direct and global illumination components, achieving the same effect without additional hardware complexity.
3Reliability
If high-frequency modulation is used to separate direct and global illumination components, then illumination separation is achieved, but acquisition time increases due to multiple sequential measurements
Solution Approach 1:
The patent employs periodic projection of structured light patterns with different spatial frequencies and phases. By projecting a sequence of periodic patterns and capturing the temporal response of the scene, the system can extract both direct and global illumination components through temporal analysis, achieving separation without requiring excessively long acquisition times.
4Measurement precision
If conventional structured light patterns are projected onto scenes with translucent materials, then shape measurement is attempted, but significant errors occur due to sub-surface scattering and low signal-to-noise ratio
Solution Approach 1:
The patent applies different illumination pattern characteristics (spatial frequencies, contrast levels) to different regions of the scene based on local material properties. For translucent regions exhibiting sub-surface scattering, the system uses lower spatial frequency patterns with higher contrast that are less susceptible to scattering effects, while maintaining high-frequency patterns for opaque regions, thereby optimizing measurement accuracy locally across the entire scene.
Data Source
AI summary
Depth values in a scene are measured by projecting sets of patterns on the scene, wherein each set of patterns is structured with different spatial frequency using different encoding functions. Sets of images of the scene is acquired, wherein there is one image for each pattern in each set. Depth values are determining for each pixel at corresponding locations in the sets of images. The depth values of each pixel are analyzed, and the depth value is returned if the depth values at the corresponding locations are similar. Otherwise, the depth value is marked as having an error.