3D Wireframe Reconstruction From 2D Aerial Image Segments
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Solution Overview
Problem
Existing methods for generating 3D models from digital images are computationally impractical, require excellent camera calibration, or result in noisy point clouds that soften important edge features, making efficient and accurate 3D structure modeling challenging.
Innovation Solution
A computer vision system that processes digital images to automatically detect and generate 3D structures using 2D segments, employing methods like cluster-based and epipolar-based segment detection, and outlier removal to create a 3D segment cloud.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If key point detectors are used to identify corresponding points from multiple images, then 3D structure inference is possible, but computational complexity becomes impractical
Solution Approach 1:
The patent segments the continuous image space into discrete line segments using the Hough transform. Instead of processing all possible key point combinations across multiple images, the method divides the detection space into manageable segment units that can be independently processed and then aggregated to form the complete 3D structure, significantly reducing computational burden while maintaining accuracy
Solution Approach 2:
The patent introduces line segments as an intermediary representation between raw image data and final 3D structure. Rather than directly inferring 3D structure from key points, the method first extracts 2D line segments as intermediate features, which then serve as the basis for 3D reconstruction. This intermediary step simplifies the computational pathway and reduces the complexity of direct 3D inference
2Reliability
If point clouds are created from multiple images with averaging to cope with noise, then noise is reduced, but edge features become softened
Solution Approach 1:
The patent segments the point cloud data into distinct line segment features using the Hough transform. By identifying and extracting linear features separately from the noisy point cloud, the method preserves sharp edge definitions while still benefiting from noise reduction through the segment-based aggregation approach, avoiding the softening effect of uniform averaging
Solution Approach 2:
The patent applies different processing qualities to different regions of the data. Edge regions identified as line segments receive specialized processing that maintains their sharpness and definition, while other regions can undergo more aggressive noise reduction. This local differentiation allows noise reduction without compromising the critical edge features that define the 3D structure
3Productivity
If 2D segments are detected and converted to 3D segments, then a manageable representation is achieved, but computational processing is required
Solution Approach 1:
The patent replaces complex manual 3D modeling processes with automated computational methods. The Hough transform and epipolar geometry algorithms automatically convert 2D line segments into 3D representations without requiring manual intervention, achieving both data manageability and reduced processing complexity through algorithmic automation
Solution Approach 2:
The patent creates simplified 2D line segment copies from the original image data, which serve as manageable representations that can be easily processed and stored. These 2D segment copies are then systematically converted to 3D segments through computational methods, providing an intermediate manageable format that reduces the complexity of direct 3D processing while preserving the essential geometric information
Data Source
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Figure 3A~3E
AI summary
A system for modeling a three-dimensional structure utilizing two-dimensional segments comprising a memory and a processor in communication with the memory. The processor extracts a plurality of two-dimensional segments corresponding to the three- dimensional structure from a plurality of images indicative of different views of the three- dimensional structure. The processor determines a plurality of three-dimensional candidate segments based on the extracted plurality of two-dimensional segments and adds the plurality of three-dimensional candidate segments to a three-dimensional segment cloud. The processor transforms the three-dimensional segment cloud into a wireframe indicative of the three-dimensional structure by performing a wireframe extraction process on the three-dimensional segment cloud.