Geoaccurate 3D Reconstruction via Relative Point Cloud Transformation
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Solution Overview
Problem
Current methods for obtaining geoaccurate three-dimensional reconstructions from image data, especially from airborne or satellite platforms, face inaccuracies due to drift in algorithms and errors introduced by sensor position and orientation data, lacking effective techniques for geoaccurate reconstruction without ground control points or digital elevation maps.
Innovation Solution
A technique that generates a high-fidelity point cloud in a relative coordinate system using structure from motion processing, then transforms it into a fixed earth-based coordinate system using a transformation matrix derived from corresponding points in a low-fidelity sparse point cloud, avoiding physical sensor model errors and external references.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If sensor position and orientation metadata (GPS/INS) are used to generate a point cloud in a fixed coordinate system, then geolocation information is provided, but errors in the sensor data introduce undesirable inaccuracies and drift in the reconstruction
Solution Approach 1:
The patent segments the point cloud generation process into two independent stages: first generating a high-fidelity point cloud in a relative coordinate system using only image-based geometry (avoiding sensor errors), then separately determining a transformation to map this to the fixed coordinate system. This segmentation isolates the high-precision geometric reconstruction from the error-prone sensor data, allowing each to be optimized independently.
Solution Approach 2:
The patent introduces a relative coordinate system as an intermediary between the image data and the fixed coordinate system. This intermediate representation allows the high-fidelity geometric relationships to be preserved first, then transformed to the fixed coordinate system using a computed transformation matrix, rather than directly using error-prone sensor metadata.
2Measurement precision
If ground control points or external digital elevation maps are used to achieve geoaccurate reconstruction, then geolocation accuracy is improved, but the complexity and manual effort of the process increases
Solution Approach 1:
The patent enables the system to self-determine its own transformation to the fixed coordinate system by computing it from the relationship between points in the high-fidelity relative point cloud and corresponding points in a low-fidelity fixed coordinate system point cloud, without requiring external ground control points or digital elevation maps. The system uses its own generated data to establish the transformation.
Solution Approach 2:
The patent changes the approach from using external reference data (GCPs, DEMs) to computing the transformation parameters directly from the reconstructed point clouds themselves. This parameter change eliminates the need for external references while maintaining geolocation accuracy.
3Ease of operation
If structure from motion processing is performed using physical sensor models with position and orientation data, then the point cloud is placed in a fixed coordinate system, but the fidelity of the reconstruction decreases due to sensor errors
Solution Approach 1:
The patent inverts the conventional approach by first generating the high-fidelity point cloud in a relative coordinate system without using sensor position and orientation data, then determining the transformation to the fixed coordinate system from the point cloud relationships themselves, rather than using sensor data to directly place points in the fixed coordinate system.
Data Source
AI summary
A technique for generating a three-dimensional reconstruction of a scene involves generating a high-fidelity point cloud representing a three-dimensional reconstruction of a scene from two-dimensional images generated by at least one sensor whose position and orientation are known relative to a fixed coordinate system for each of the images. The high-fidelity point cloud is generated in a relative coordinate system without regard to the position and orientation of the sensor(s). A low-fidelity point cloud is generated in the fixed coordinate system from the two-dimensional images using the position and orientation of the sensor(s) relative to the fixed coordinate system. A transformation between the relative and fixed coordinate systems is determined based on a relationship between points in the high-fidelity and low-fidelity point clouds, and the high-fidelity point cloud is converted from the relative coordinate system to the fixed coordinate system by applying the transformation to the high-fidelity point cloud.