Unposed Camera Point Cloud Construction via Stereo Pair Scaling
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Solution Overview
Problem
Existing techniques fail to accurately merge and scale three-dimensional point clouds from images taken from multiple unknown camera positions, resulting in unsatisfactory approximations of original structures and requiring all scene features to be visible in all imagery.
Innovation Solution
A method for processing stereo rectified images involves selecting pairs of images, determining point clouds, aligning features with a reference, and scaling subsequent point clouds to create a single, correlated point cloud, even when cameras are unposed and move around an area of interest from various directions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing techniques are used to merge point clouds from multiple images, then the merging process can be performed, but the result only roughly approximates the original structures and requires all scene features to be visible in all imagery
Solution Approach 1:
The patent segments the point cloud merging process into multiple stages: first creating individual point clouds from stereo pairs, then progressively merging them through coordinate transformations. This segmentation allows each stereo pair to be processed independently without requiring all features to be visible in all images, resolving the contradiction between accuracy and visibility requirements.
Solution Approach 2:
The patent introduces an intermediary coordinate system and transformation process that mediates between different stereo pairs. By using reference features and coordinate transformations as intermediaries, the system can merge point clouds from stereo pairs that don't share all features, eliminating the requirement that all scene features be visible in all imagery while maintaining accuracy.
2Ease of operation
If camera positions are unknown, then the system can operate without precise positioning, but the physical size of the point cloud remains unknown and must be defined as unscaled
Solution Approach 1:
The patent performs preliminary coordinate transformations and feature matching between stereo pairs before final scaling. By establishing relative coordinate systems and identifying reference features in advance, the system prepares the data structure needed for accurate scaling even when absolute camera positions are unknown, resolving the contradiction between ease of operation and measurement precision.
3Area of stationary object
If the camera is moved to multiple positions to create multiple stereo pairs, then more scene coverage is achieved, but rescaling, rotating, and merging point clouds becomes increasingly challenging
Solution Approach 1:
The patent segments the multi-position point cloud merging into a systematic process where each stereo pair is processed independently to create local point clouds, then merged through coordinate transformations. This segmentation reduces processing complexity by breaking down the challenging multi-position merging into manageable sequential steps, allowing extensive scene coverage without proportionally increasing complexity.
Solution Approach 2:
The patent transforms the problem from three-dimensional spatial merging to a coordinate system transformation problem. By introducing reference features and using two-dimensional image coordinate transformations to establish three-dimensional point cloud relationships, the system manages the complexity of merging point clouds from multiple camera positions while achieving comprehensive scene coverage.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach enables the construction of accurate, scaled three-dimensional point clouds from images taken from multiple camera positions, including scenarios with unknown camera locations and orientations, and handles cases where not all features are visible from all positions, providing a robust method for merging and scaling point clouds.
Implementation Method 1
any pair of images, taken from two different positions, contains parallax information relating to the range to various objects in the scene
Data Source
AI summary
A method for processing stereo rectified images, each stereo rectified image being associated with a camera position, the method including selecting a first pair of stereo rectified images; determining a first point cloud of features from the pair of stereo rectified images; determining the locations of the features of the first point cloud with respect to a reference feature in the first point cloud; selecting a second pair of stereo rectified images so that one stereo rectified image of the second pair is common to the first pair, and scaling a second point cloud of features associated with the second pair of stereo rectified images to the first point cloud of features.