3D Point Cloud Boundary Estimation Using Motion-Based OOI Isolation
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Solution Overview
Problem
Existing methods struggle to accurately extract the boundaries of an object of interest (OOI) from a large point cloud generated by a drone, as they fail to distinguish OOI points from background objects, leading to inefficiencies in modeling and documentation of telecommunication equipment like cell towers.
Innovation Solution
A method involving the estimation of the center of the OOI on the X-Y plane and identification of offsets along the z-axis, using a truncated point cloud formed by selecting points based on motion metrics from multiple images, to isolate relevant points and generate a reduced point cloud.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Area of stationary object
If a large point cloud is generated to capture the entire area surrounding the OOI, then the coverage and completeness of the model is improved, but the difficulty of automatically extracting OOI points increases
Solution Approach 1:
The patent segments the large point cloud into multiple smaller subsets based on spatial distribution and motion characteristics. By dividing the point cloud into manageable chunks and processing them separately using motion metrics from multiple images, the system maintains comprehensive coverage while reducing the complexity of extracting OOI points from the entire dataset at once.
Solution Approach 2:
The patent introduces motion metrics as a new parameter to distinguish OOI points from background points. By calculating motion characteristics across multiple images and using these metrics to filter and identify OOI points, the system transforms the extraction problem from a purely spatial task to one that incorporates temporal motion information, thereby improving extraction accuracy in large point clouds.
2Measurement precision
If motion metrics are calculated for all N points using K images, then the accuracy of boundary estimation is improved, but the computational complexity increases
Solution Approach 1:
The patent divides the set of N points into multiple subsets and processes each subset separately. Instead of calculating motion metrics for all points simultaneously, the system processes points in groups, reducing the computational burden at each step while maintaining overall accuracy through the aggregation of results from all subsets.
Solution Approach 2:
The patent calculates motion metrics for a representative subset of points rather than exhaustively processing every single point with full computational detail. By using sampling and subset-based processing, the system achieves sufficient boundary estimation accuracy without the excessive computational cost of processing every point at maximum detail.
3Productivity
If the point cloud is reduced to a subset containing only P points, then the processing efficiency is improved, but the risk of losing relevant OOI data increases
Solution Approach 1:
The patent uses motion metrics as a filtering parameter to selectively retain points that exhibit characteristics consistent with OOI. By calculating motion metrics for points and using these metrics to identify and keep only those points with relevant motion patterns, the system reduces the point cloud to a manageable subset while maintaining high confidence that OOI data is preserved.
Solution Approach 2:
The patent employs a feedback mechanism where motion metrics calculated from multiple images are used to iteratively refine the point cloud subset. Points are evaluated based on their motion characteristics, and the system feedback-loops to adjust the subset selection, ensuring that points are retained or removed based on their contribution to accurate OOI boundary estimation rather than arbitrary reduction.
Data Source
AI summary
A method for estimating boundaries of an OOI. The method includes obtaining a first point cloud comprising a set of N points. The method also includes obtaining a set of K images. The method also includes, for each point included in the set of N points, identifying, for each one of the K images, the point's location within a 2D space corresponding to the image, thereby obtaining, for each point included in the set of N point, K location identifiers. The method also includes, for each point included in the set of N points, determining a motion metric for the point using the K location identifiers for the point. The method also includes using the N motion metrics to form a subset of the N points. The method also includes, for each point included in the subset of points, obtaining a location identifier for the point. The method also includes using the location identifiers to estimate a center of the OOI and then using the estimated center of the OOI to identify boundaries for the OOI.