In-line Error Correction for Stereo Depth and Motion Estimation
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Solution Overview
Problem
Current techniques for hierarchical motion estimation (ME) and hierarchical depth from stereo (DFS) fail to address errors introduced early in the process, leading to poor quality results due to characteristics like repeating patterns, flat/featureless regions, and aperture issues in input content.
Innovation Solution
A method that identifies error regions in-line during the ME or DFS process, performs iterative downscale passes, and generates a global motion buffer to correct errors, ensuring high-quality final outputs by refining the motion estimation and depth estimation processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If hierarchical motion estimation and depth from stereo are performed without error correction, then processing speed is maintained, but measurement precision deteriorates due to errors from repeating patterns, flat regions, and aperture issues
Solution Approach 1:
The patent applies preliminary action by identifying error regions and performing error correction during the hierarchical motion estimation process itself, rather than correcting errors after the fact. The system proactively detects and corrects errors from repeating patterns, flat regions, and aperture issues during the estimation process, improving measurement precision without significantly increasing overall processing complexity
Solution Approach 2:
The patent implements feedback mechanisms where the system continuously monitors motion estimation results and adjusts processing based on detected errors. Error regions are identified and fed back into the correction process, allowing the system to refine motion vectors and depth values iteratively, thereby improving precision while managing complexity through targeted corrections
2Measurement precision
If error correction is performed in-line during hierarchical ME process, then measurement precision improves, but processing time increases
Solution Approach 1:
The patent segments the motion estimation process into distinct phases: initial hierarchical ME, error region identification, and targeted correction passes. By dividing the process this way, the system can perform corrections only where needed rather than reprocessing everything, reducing the time penalty while maintaining improved precision in error-prone regions
Solution Approach 2:
The patent applies local quality by focusing error correction only on specific error regions identified during processing, rather than uniformly applying corrections across the entire image. This targeted approach corrects motion vectors in regions affected by repeating patterns, flat regions, and aperture issues without unnecessarily processing error-free areas, thereby reducing overall processing time while maintaining high precision where needed
3Manufacturing precision
If iterative downscale passes are performed to correct errors, then manufacturing precision improves, but device complexity increases
Solution Approach 1:
The patent applies dimensionality change by performing downscale passes at different resolution levels. The system iteratively processes the image at progressively lower resolutions, correcting depth estimation errors in a coarse-to-fine manner. This dimensional approach simplifies the correction process at each stage while achieving high final precision, managing algorithm complexity through hierarchical processing
Data Source
AI summary
This disclosure provides systems, devices, apparatus, and methods, including computer programs encoded on storage media, for in-line error correction for ME and DFS. A processor identifies, in-line, a set of error regions associated with a first frame. The processor performs, in-line and based on the identified set of error regions and partial ME results of a first ME pass and/or partial DFS results of a first DFS pass, a set of iterative downscale passes on the partial ME results and/or the partial DFS results. The processor generates, in-line, a global motion buffer based on the performed set of iterative downscale passes. The processor performs, based on the global motion buffer and/or the identified set of error regions, a second ME pass and/or a second DFS pass.