Point Cloud Visibility Determination via Inversion and Convex Hull
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Solution Overview
Problem
Current methods for determining visible points in a point cloud require significant computational resources and time, especially when reconstructing surfaces, and are not efficient for camera rotation or changes in the field of view, with no solutions achieving O(n log n) runtime complexity.
Innovation Solution
The method involves inverting a portion of the image to create an inversed object, determining its convex hull, and assessing point visibility based on the position of parallel points within the convex hull relative to their neighbors, allowing for O(n log n) complexity and independence from camera rotation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If surface reconstruction is performed to determine visible points, then visibility determination accuracy is improved, but computational time and resources increase significantly
Solution Approach 1:
The patent extracts only the essential geometric information needed for visibility determination by directly processing point cloud data to identify visible points, rather than performing complete surface reconstruction. This extraction approach obtains visibility information without the overhead of building full surface models, thereby reducing computational time while maintaining accuracy.
Solution Approach 2:
The patent inverts the traditional approach by not trying to determine which points are visible through surface reconstruction, but rather directly identifying visible points through geometric relationships in the point cloud. This inversion of the problem-solving approach eliminates the need for surface reconstruction while achieving the same visibility determination goal.
2Measurement precision
If traditional visibility determination methods are used, then comprehensive visibility analysis is achieved, but device complexity and memory usage increase
Solution Approach 1:
The patent extracts only the necessary geometric relationships from the point cloud data required for visibility determination, avoiding the complexity of full surface reconstruction algorithms. By extracting essential visibility information directly from point relationships, the method reduces algorithmic complexity and memory requirements while maintaining comprehensive visibility analysis.
3Adaptability or versatility
If camera rotation or field of view changes occur, then viewing flexibility is improved, but visibility recalculation time increases
Solution Approach 1:
The patent performs preliminary organization of point cloud data into spatial structures that enable efficient visibility queries from multiple viewpoints. By pre-processing the point cloud into an optimized data structure, the system can rapidly recalculate visibility for different camera rotations and field of view changes without performing complete重新 analysis, thus reducing recalculation time while maintaining viewing flexibility.
Data Source
AI summary
A method for determining an optimal location for positioning an image capturing device within a volume, the method including, obtaining a plurality of points to be visible from the image capturing device, performing inversion on points located in the vicinity of the plurality of points thus creating a computerized inversed object, each point in the vicinity of the plurality of point is translated to a corresponding point in the computerized inversed object, defining a convex hull of the inversed object, determining if a point of the plurality of points is visible from the viewpoint according to the position of its corresponding point on the convex hull relative to its neighbor points, repeating said determining for multiple locations within the volume, determining whether a predetermined set of points is visible from each location, selecting the optimal location of the image capturing device based on the results of said repeated determining.