Light Field Depth Acquisition via Color-Filtered Wavefront Processing
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Solution Overview
Problem
Existing light field imaging technologies face challenges in acquiring and processing 3D images due to the limitations of current hardware and computational methods, which result in large data sizes, complex reconstruction algorithms, and the need for additional sensors, leading to increased power consumption and device footprint, while also being sensitive to interfering signals and specular reflections.
Innovation Solution
A method involving a diffraction grating pattern to diffract an optical wavefront, using a pixel array with color filters to parse intensity and phase information, and applying color radial transfer functions for depth mapping, allowing direct measurement of chromatic dependence and angle of incidence, enabling efficient compression and reconstruction of 3D images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple sensors or sensor segmentation are employed to accommodate various fields of view for depth discernment, then depth information can be obtained, but overall image quality deteriorates and sensor footprint increases
Solution Approach 1:
The patent segments the sensor array into multiple regions, each dedicated to capturing light from specific angular ranges. This segmentation allows depth information to be extracted from spatially distributed measurements while using a single integrated sensor chip, avoiding the need for multiple separate sensors and reducing overall footprint.
Solution Approach 2:
The patent captures light field information by measuring not only intensity but also the angular distribution of incoming light rays. By adding angular dimension measurement to the traditional 2D intensity capture, depth information is obtained without requiring additional sensors, as the angular data encodes depth cues inherently.
2Loss of information
If traditional computing and mobile phone systems are used to handle uncompressed light field images, then full resolution data can be processed, but bus bandwidth and memory transfer rates are exceeded and storage space is insufficient
Solution Approach 1:
The patent extracts only the essential light field parameters needed for depth reconstruction and 3D imaging, rather than processing complete uncompressed light field images. By extracting angular distribution data and intensity measurements at key sampling points, the system achieves efficient processing with reduced data throughput requirements.
Solution Approach 2:
The patent performs preliminary processing of light field data at the sensor level, organizing angular and spatial information into structured formats before transmission to the processor. This preliminary organization reduces the computational burden on downstream systems and enables efficient compression and reconstruction.
3Productivity
If compressed sensing and non-linear reconstruction techniques are applied to reduce data size, then storage and processing become feasible, but computational expense increases massively
Solution Approach 1:
The patent changes the measurement parameters from capturing complete high-resolution images to measuring angular distributions and intensity at strategically sampled points. This parameter transformation enables efficient compression by reducing the dimensionality of the data while preserving the information needed for depth and 3D reconstruction.
4Measurement precision
If active infrared emitters and detectors are added to enable time-of-flight or structured-light depth sensing, then depth measurement capability is improved, but power consumption and device footprint increase
Solution Approach 1:
The patent enables the sensor system to extract depth information from passive reflected light without requiring active illumination. By analyzing the angular distribution of incoming light rays and their intensity variations, the system derives depth cues inherently present in the scene, making the sensor self-sufficient and eliminating the need for additional active components.
5Measurement precision
If active infrared illumination techniques are used for depth sensing, then depth information can be acquired, but sensitivity to interfering signals, specular reflections, and ambient infrared light increases
Solution Approach 1:
The patent uses passive sensing modalities that rely on naturally occurring light in the scene rather than active infrared illumination. This approach makes the system inherently more robust to interfering signals and ambient infrared light, as it measures angular distributions of reflected light across multiple wavelengths and directions, providing depth information without the vulnerabilities of active illumination techniques.
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
Enables efficient compression and reconstruction of 3D images with reduced computational expense, preserving 2D image quality, and eliminating the need for multiple sensors or active lighting systems, while being compatible with passive sensing modalities and existing image sensors.
Implementation Method 1
diffracting an optical wavefront originating from the scene according to a diffraction grating pattern of a diffraction grating having a grating period along a grating axis, to generate a diffracted optical wavefront
Implementation Method 2
detecting the diffracted optical wavefront in a near-field Fresnel diffraction regime
Implementation Method 3
the color filters forming a color mosaic defining a primary color and one or more secondary colors arranged such that different neighboring pixels associated with the primary color detect different spatial parts of the diffracted optical wavefront
Data Source
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AI summary
Techniques for capturing three-dimensional image data of a scene and processing light field image data obtained by an optical wavefront sensor in 3D imaging applications are provided. The disclosed techniques provide a depth map of an observable scene from light field information about an optical wavefront emanating from the scene, and make use of color filters forming a color mosaic defining a primary color and one or more secondary colors, and color radial transfer functions calibrated to provide object distance information from the spatio-spectrally sampled pixel data.