Point Cloud Scanner Assignment via Disk Intersection Analysis
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Solution Overview
Problem
Traditional techniques cannot process point clouds to generate high-resolution 3D meshes without visibility information, as this metadata is often missing, preventing applications from determining which scanner has visibility to each point in the cloud.
Innovation Solution
A process that computes a scale and non-oriented normal for each point, creates disks for efficient intersection analysis, and assigns points to scanner positions using an AABB tree, excluding false positives and determining the best view among multiple scanners.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional techniques are used to process point clouds, then the processing can be performed with simple methods, but the visibility information is missing preventing generation of high-resolution 3D meshes
Solution Approach 1:
The patent segments the point cloud processing into distinct phases: creating disks for each point, organizing them in an AABB tree structure, performing ray-casting queries, and assigning visibility information. This segmentation transforms the complex problem of visibility determination into manageable discrete steps, resolving the contradiction between reliability and complexity.
Solution Approach 2:
The patent performs preliminary actions by pre-computing disk representations for all points and organizing them in an AABB tree before actual visibility queries are executed. This preliminary preparation enables efficient O(log n) queries during processing, achieving both high reliability in visibility determination and computational efficiency.
2Measurement precision
If visibility information is computed for each point, then accurate scanner assignment is achieved, but computational time and resources increase significantly
Solution Approach 1:
The patent introduces disks as intermediary geometric representations between the raw point cloud data and the visibility queries. These disks serve as mediators that enable efficient spatial indexing through the AABB tree, allowing precise scanner assignment without exhaustive computation for each point-query pair.
Solution Approach 2:
The patent replaces the traditional mechanical approach of checking every point against every scanner position with a computational geometry approach using AABB trees and ray-casting algorithms. This substitution reduces the computational complexity from O(n*m) to O((n+m) log n), significantly reducing processing time while maintaining measurement precision.
3Productivity
If disks are created for each point with computed scales and normals, then efficient intersection analysis is enabled, but the device complexity and memory usage increase
Solution Approach 1:
The patent transforms the point cloud data into a different dimensional representation by creating disks with spatial extents and orientations. This dimensional transformation enables the use of AABB trees and ray-casting techniques that operate efficiently in 3D space, achieving O(log n) query performance while the added structural complexity is justified by the productivity gain.
Data Source
AI summary
In an example embodiment, a process may create a disk for each point of the point cloud, where a size of the disk and the orientation of the disk are respectively based on a computed scale and a non-oriented normal. The process may insert each disk into a search structure that is queried to determine if one or more disks intersect the paths from a given point to each scanner in the point cloud. The process may create an output corresponding to each scanner in the point cloud, where each output includes the number of intersecting disks and the distance from the given point to the scanner. The process may implement a sorting algorithm to assign the given point to a scanner position. An application may utilize the assignment of each point in the point cloud to a scanner position to generate a high-resolution 3D mesh of a scene.