Time-of-Flight Depth Imaging Pixel Alignment Iterative Correction
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Solution Overview
Problem
Existing depth camera systems face challenges in accurately determining object depth due to differences in reflectivity and noise, particularly when using multiple sensors or capturing images at different times, which can lead to pixel misalignment and inaccurate depth measurements.
Innovation Solution
An iterative process is employed to align and refine depth images by modifying one light intensity image based on a smoothed depth image, calculated from two light intensity images captured at different places or times, to compensate for reflectivity differences and noise, allowing for high-quality depth image generation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If two sensors are used to take depth measurements at the same time, then measurement accuracy is improved by eliminating temporal motion differences, but device complexity increases and pixel alignment becomes more difficult due to parallax differences
Solution Approach 1:
The patent introduces an iterative correlation process as an intermediary mechanism that mediates between the two sensors' data. This process gradually adjusts and aligns pixel values from both sensors through repeated correlation attempts, effectively bridging the parallax difference without requiring perfect initial alignment or complex calibration hardware
Solution Approach 2:
The patent performs preliminary actions by initially capturing depth measurements from both sensors before attempting correlation. The system prepares the raw depth data from both sensors and then applies the iterative correlation process, allowing the alignment to be established through software processing rather than requiring precise physical positioning
2Stability of the object's composition
If two sensors are used to take depth measurements simultaneously, then object motion between measurements is eliminated, but pixel alignment becomes difficult due to parallax differences between sensor locations
Solution Approach 1:
The patent applies dynamics by making the correlation process adaptive and iterative rather than static. The system dynamically adjusts pixel values through multiple iterations, allowing the alignment to evolve and converge based on the actual data characteristics, making the process robust to varying parallax conditions
Solution Approach 2:
The iterative correlation process incorporates feedback by using the correlation results from each iteration to guide subsequent adjustments. The system continuously refines the alignment based on how well the pixel values correlate, creating a closed-loop system that self-corrects for parallax differences
3Measurement precision
If light intensity images are used to determine depth, then reflectivity differences between objects cause measurement inaccuracies, but using multiple measurements to compensate increases processing complexity
Solution Approach 1:
The patent merges multiple depth measurements through the iterative correlation process, combining information from both sensors to produce a single aligned depth image. This merging approach naturally compensates for reflectivity differences by using the complementary information from both measurements, achieving robustness without requiring separate complex processing pipelines
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 enables the generation of accurate and high-quality depth images by aligning pixels and reducing noise, even when using multiple sensors or capturing images at different times, thereby improving the precision of depth measurements.
Implementation Method 1
The depth camera may project light onto an object in the camera's field of view. The light reflects off the object and back to one or more image sensors in the camera, which collect light intensity.
Implementation Method 2
One technique for determining distance to the object is based on the round trip time-of-flight of the light.
Data Source
AI summary
Techniques are provided for determining depth to objects. A depth image may be determined based on two light intensity images. This technique may compensate for differences in reflectivity of objects in the field of view. However, there may be some misalignment between pixels in the two light intensity images. An iterative process may be used to relax a requirement for an exact match between the light intensity images. The iterative process may involve modifying one of the light intensity images based on a smoothed version of a depth image that is generated from the two light intensity images. Then, new values may be determined for the depth image based on the modified image and the other light intensity image. Thus, pixel misalignment between the two light intensity images may be compensated.