Hierarchical Neural Displacement Map Generation
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Solution Overview
Problem
Existing methods for generating disparity maps from stereo images are computationally costly and complex, with high implementation complexity and high calculation costs, limiting their effectiveness in real-world applications such as autonomous vehicles and augmented reality.
Innovation Solution
A method and apparatus using a neural network-based feature extractor to generate a displacement map by processing input datasets, employing a hierarchical feature map hierarchy with refinement pairs to produce high-quality disparity maps efficiently, while reducing computational costs through strategic upsampling and warping operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If neural networks are used for generating disparity maps from stereo images, then measurement precision is improved, but device complexity and calculation costs increase
Solution Approach 1:
The patent segments the disparity map generation process into multiple hierarchical levels, processing images at different resolutions (e.g., 1/4, 1/8, 1/16 of original size). This segmentation allows neural networks to operate on smaller, more manageable data sets at each level, reducing computational complexity while maintaining accuracy through progressive refinement from coarse to fine details.
2Measurement precision
If high-resolution disparity maps are generated directly, then measurement precision is improved, but calculation costs and processing time increase
Solution Approach 1:
The patent applies preliminary action by first generating low-resolution disparity maps at coarser hierarchical levels before processing higher resolutions. The coarse-level disparity information serves as a preliminary result that guides and constrains subsequent fine-level processing, reducing the search space and computational burden at each refinement stage while ensuring final high-resolution accuracy.
3Measurement precision
If multiple hierarchical levels of feature maps are processed, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent merges results from multiple hierarchical levels through a unified processing framework. Disparity information from different resolution levels is combined and refined systematically, where coarser levels provide global structure and finer levels add local detail. This merging approach achieves high precision while managing complexity through structured integration rather than independent processing of each level.
Data Source
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AI summary
The invention is a method and an apparatus for generating a displacement map of a first input dataset and a second input dataset of an input dataset pair. The apparatus comprises - a neural network based feature extractor (25) for processing the first input dataset and the second input dataset so as to generate a feature map hierarchy (50) comprising a base pair of feature maps (20e, 30e) and a refinement pair of feature maps (20b, 30b, 20c, 30c, 20d, 30d), - a displacement unit comprising a first comparator unit for obtaining an initial displacement map using the base pair of feature maps (20e, 30e); and - a displacement refinement unit for obtain an updated displacement map for the refinement pair of feature maps (20d, 30d).