3D Building Reconstruction Using AR Anchors and Pose Scaling
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Solution Overview
Problem
Existing 3-D reconstruction methods for building structures face challenges with large image inputs requiring costly computing resources and sparse data failing to capture accurate information, while augmented reality (AR) devices suffer from sensor drift and noise, leading to location inaccuracies and lack of integration with other data for realistic rendering.
Innovation Solution
A method leveraging augmented reality frameworks to capture images of a building using sparse or dense image sets, incorporating non-camera anchors and reference poses, and utilizing AR camera data with real-world poses to generate accurate 3-D models by selecting candidate poses and applying a scaling factor based on geometric translation ratios.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If video input or large image sets are used for 3-D reconstruction, then measurement accuracy and rendering realism are improved, but computing cost and resource requirements increase significantly
Solution Approach 1:
The patent extracts only the essential geometric information needed for 3-D reconstruction from image data, using feature detection and matching to identify key points rather than processing entire images. This selective extraction of relevant data reduces computing requirements while maintaining measurement accuracy.
Solution Approach 2:
The system uses a moderate number of images (neither sparse nor excessive) to achieve sufficient reconstruction quality. By capturing images at multiple viewpoints and processing only the necessary geometric relationships, the system avoids both data scarcity and computational overload.
2Loss of energy
If sparse image sets are used for 3-D reconstruction, then computing resources are reduced, but adequate information for realistic rendering and accurate measurements is not captured
Solution Approach 1:
The patent replaces traditional photogrammetry methods with augmented reality frameworks that use sensor fusion (camera, accelerometer, gyroscope, magnetometer) to determine device pose and capture images. This substitution enables efficient use of sparse images by leveraging additional sensor data for geometric reconstruction.
Solution Approach 2:
The system changes the parameters of image capture by using AR frameworks to control timing, positioning, and angles of captured images. This structured approach ensures that even sparse images contain sufficient geometric information for accurate 3-D reconstruction.
3Ease of operation
If augmented reality frameworks are used for image capture, then device accessibility and ease of operation are improved, but sensor drift and noise cause location inaccuracies
Solution Approach 1:
The patent merges data from multiple sensors (camera, accelerometer, gyroscope, magnetometer) and multiple image sources to determine device pose and capture images. This fusion of multiple data streams compensates for individual sensor drift and noise, improving location accuracy while maintaining ease of operation.
Solution Approach 2:
The system uses feedback from AR framework tracking and sensor data to continuously adjust and refine device pose estimation. By comparing expected versus actual sensor readings and correcting for drift over time, the system maintains accurate location information despite individual sensor inaccuracies.
Data Source
AI summary
System and method are provided for scaling a 3-D representation of a building structure. The method includes obtaining images of the building structure, including non-camera anchors. The method also includes identifying reference poses for images based on the non-camera anchors. The method also includes obtaining world map data including real-world poses for the images. The method also includes selecting candidate poses from the real-world poses based on corresponding reference poses. The method also includes calculating a scaling factor for a 3-D representation of the building structure based on correlating the reference poses with the selected candidate poses. Some implementations use structure from motion techniques or LiDAR, in addition to augmented reality frameworks, for scaling the 3-D representations of the building structure. In some implementations, the world map data includes environmental data, such as illumination data, and the method includes generating or displaying the 3-D representation.