Stereo Reconstruction Pipeline Depth Map Alignment
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Solution Overview
Problem
Existing stereo reconstruction pipelines amplify initial errors, leading to deteriorated final reconstruction results due to the cascading and amplification of inaccuracies throughout the processing pipeline.
Innovation Solution
A novel stereo reconstruction pipeline that aligns depth maps by transforming them to minimize an objective function, identifying and reducing the influence of outlier points, and merging depth maps to generate a three-dimensional model, thereby improving alignment and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If traditional stereo reconstruction pipeline is used, then processing speed is maintained, but error accumulation and amplification occur leading to deteriorated reconstruction quality
Solution Approach 1:
The patent applies preliminary action by performing depth map alignment and outlier identification before the final 3D model generation. Specifically, depth maps are aligned through transformation operations, and outlier points are identified and down-weighted before merging, preventing error propagation to subsequent processing stages and improving final reconstruction accuracy
Solution Approach 2:
The patent introduces intermediary processing steps between image input and final 3D reconstruction. Depth map alignment and outlier identification act as intermediary operations that mediate between raw depth data and the final model, filtering and correcting errors before they affect the reconstruction quality
2Measurement precision
If depth maps are transformed to improve alignment, then alignment accuracy is improved, but computational complexity increases
Solution Approach 1:
The patent changes parameters of depth maps through transformation operations (rotation, translation, scaling) to improve alignment. By adjusting these geometric parameters, the system optimizes the correspondence between depth maps from different views, enhancing alignment accuracy while managing computational requirements through efficient transformation algorithms
3Manufacturing precision
If outlier points are identified and removed, then model quality is improved, but processing time increases
Solution Approach 1:
The patent applies local quality by identifying and treating outlier points locally rather than processing the entire depth map uniformly. Outlier points are detected through statistical analysis of depth values and their consistency across multiple views, and only these specific points are down-weighted or removed, preserving processing efficiency while improving local model quality
Data Source
AI summary
A novel stereo reconstruction pipeline that features depth map alignment and outlier identification is provided. One example method includes obtaining a plurality of images depicting a scene. The method includes determining a pose for each of the plurality of images. The method includes determining a depth map for each of the plurality of images such that a plurality of depth maps are determined. Each of the plurality of depth maps describes a plurality of points in three-dimensional space that correspond to objects in the scene. The method includes aligning the plurality of depth maps by transforming one or more of the plurality of depth maps so as to improve an alignment between the plurality of depth maps. The method includes identifying one or more outlying points. The method includes generating a three-dimensional model of the scene based at least in part on the plurality of depth maps.