Hybrid Recursive Analysis for 3D Reconstruction Depth Resolution
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Solution Overview
Problem
Conventional patch sweeping methods face challenges in defining a search range in the parameter space to achieve high depth resolution with reasonable computational effort for real-time 3D reconstruction from multiple images.
Innovation Solution
The proposed method employs a hybrid recursive analysis based on spatio-temporal objects, which reduces computational effort by testing a limited number of hypotheses and updates these hypotheses to improve matching criteria, using a multi-hypotheses test and correspondence analysis to determine 3D information from multiple images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If patch sweeping methods test multiple surflet orientations to achieve high depth resolution, then measurement precision is improved, but computational effort increases significantly
Solution Approach 1:
The patent performs preliminary actions by establishing coarse-to-fine hypotheses about 3D object parameters before detailed matching. The system first identifies candidate spatio-temporal objects with approximate parameters, then refines these hypotheses iteratively. This preliminary structuring of the search space allows the system to achieve high depth resolution without exhaustively testing all possible surflet orientations, thereby reducing computational effort while maintaining measurement precision.
Solution Approach 2:
The patent segments the 3D reconstruction problem into multiple hierarchical levels: first identifying candidate objects with coarse parameters, then progressively refining depth and orientation estimates through multiple matching stages. This segmentation of the parameter search space into hierarchical levels allows the system to achieve high depth resolution at critical regions without uniformly processing all areas at maximum detail, thus optimizing the balance between measurement precision and computational effort.
2Adaptability or versatility
If a large search range is defined in parameter space to capture all possible 3D objects, then adaptability is improved, but computational effort increases
Solution Approach 1:
The patent implements dynamic adaptation of the search range by adjusting hypothesis parameter ranges based on local image content and previous matching results. The system dynamically expands or contracts the search space in different regions depending on object complexity, motion characteristics, and matching confidence. This dynamic approach maintains high adaptability for diverse 3D objects while avoiding exhaustive search in regions where the object parameters are already well-constrained, thereby optimizing computational effort.
Solution Approach 2:
The patent applies different search range strategies to different spatial regions and object types. In regions with high motion or complex geometry, the system uses broader search ranges to ensure adaptability, while in stable, simple regions, it uses constrained search ranges to reduce computational effort. This local quality approach allows the system to maintain high adaptability overall while optimizing computational resources by applying appropriate search intensity only where needed.
3Measurement precision
If multiple hypotheses are tested and refined through updating to improve matching criterion fulfillment, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent implements feedback mechanisms where matching criterion results from initial hypothesis testing are used to guide subsequent hypothesis refinement. The system evaluates matching quality metrics (such as correlation coefficients or error measures) and uses this feedback to iteratively adjust hypothesis parameters, focusing computational resources on hypotheses that show promise. This feedback-driven approach improves measurement precision by systematically refining correct hypotheses while avoiding unnecessary complexity from testing all possible parameter combinations equally.
Solution Approach 2:
The patent uses successful hypothesis patterns from previously processed frames or regions as templates for current processing. By copying and adapting proven spatio-temporal object models from similar contexts, the system reduces the complexity of hypothesis generation while maintaining high measurement precision. Instead of independently deriving all hypotheses from scratch, the system leverages temporal and spatial correlations to reuse validated object models, thereby reducing algorithmic complexity while improving matching accuracy.
Data Source
AI summary
A method for generating 3D-information from multiple images showing a 3D scene from multiple perspectives has: providing at least two hypotheses for the 3D-information; performing a multi-hypotheses test by matching the at least two hypotheses to the multiple images and determining a test-result hypothesis that fulfills a particular matching criterion; updating the test-result hypothesis by varying a parameter set of the test-result hypothesis to further improve the matching criterion or another criterion; and determining the 3D-information on the basis of the parameter set of a resulting hypothesis provided by the action of updating the test-result hypothesis. A corresponding computer readable digital storage medium and a 3D-information generator are also described. Further embodiments perform a correspondence analysis between projections of spatio-temporal objects (STO) in multiple images to select a particular spatio-temporal object on the basis of said correspondence analysis.