Point Cloud Upsampling via Tangent Plane Proximity
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Solution Overview
Problem
Existing point cloud up-sampling methods, such as polynomial fitting, are computationally complex and unsuitable for real-time applications like on-the-fly rendering and decoding, especially in dynamic point cloud compression where under-sampling leads to noisy and irregular surfaces.
Innovation Solution
A method that detects under-sampled regions in a point cloud using a desirable sampling rate and inserts neighboring points based on tangent plane proximity, reducing complexity through local point insertion and employing Principal Component Analysis for tangent plane determination, while avoiding noisy points by threshold-based conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If polynomial fitting is used for point cloud up-sampling, then up-sampling quality is improved, but computational complexity increases
Solution Approach 1:
The patent divides the point cloud processing into two distinct phases: an offline preprocessing phase where polynomial fitting is performed to generate control points and tangent planes, and an online rendering phase where only simple geometric calculations are performed. This segmentation allows complex computations to be done once beforehand, while real-time operations remain computationally light.
Solution Approach 2:
The patent performs polynomial fitting and generates control points and tangent planes in advance during an offline preprocessing step. These pre-computed elements are then stored and reused during real-time rendering, eliminating the need to perform complex polynomial fitting operations during time-critical rendering processes.
2Manufacturing precision
If polynomial fitting is used for point cloud up-sampling, then surface accuracy is improved, but processing speed decreases
Solution Approach 1:
The patent separates surface reconstruction into offline preprocessing (polynomial fitting for accuracy) and online rendering (simple geometric interpolation for speed). This segmentation allows high-accuracy surface modeling to be performed once, while real-time rendering uses computationally efficient geometric calculations based on pre-computed control points and tangent planes.
Solution Approach 2:
The patent performs polynomial fitting and surface modeling in advance during offline preprocessing, generating control points and tangent planes that capture the essential surface geometry. During real-time rendering, only simple geometric operations are needed, achieving both high surface accuracy and fast processing speed.
3Productivity
If under-sampled regions are not corrected, then data processing speed is maintained, but rendering quality deteriorates
Solution Approach 1:
The patent applies different processing strategies to different regions of the point cloud: under-sampled regions receive special treatment through control point generation and tangent plane-based interpolation, while well-sampled regions can use simpler rendering approaches. This local quality approach ensures high rendering quality in critical areas without unnecessarily processing all regions at maximum complexity.
Data Source
Figure 1
Figure 2~3
AI summary
This method for up-sampling a point cloud representing a 3D object, comprises: - detecting (2) points belonging to at least one under-sampled region of the point cloud on the basis of at least one desirable sampling rate (K); - obtaining (12), for each detected point, an associated tangent plane; - inserting (14) in the point cloud at least one neighboring point of each detected point if a distance between the neighboring point and the tangent plane associated with the detected point is less than a first threshold.