Dynamic Point Cloud Decimation for 3D Data Optimization
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Handling large point clouds with millions or billions of data points can overwhelm computer systems, leading to slow performance, unresponsiveness, or crashes due to memory and processing resource constraints.
Innovation Solution
Dynamic decimation methods that intelligently reduce the number of data points by selectively removing, replacing, or modifying them based on positional and non-positional characteristics, such as density, color commonality, and material properties, while preserving important features and details, thereby reducing the computational load.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the number of data points in a point cloud is increased to improve representation quality, then the accuracy and detail of the 3D representation is improved, but the memory and processing resources required increase, causing system slowdown or crash
Solution Approach 1:
The patent applies local quality by differentiating treatment of data points based on their spatial location and importance. Critical regions (edges, corners, surfaces) retain high-density data points for accurate representation, while uniform regions use lower-density sampling. This selective approach maintains representation quality where needed while reducing overall data quantity to manage memory and processing resources.
Solution Approach 2:
The patent implements dynamic decimation that adapts the level of detail based on processing requirements and data characteristics. The system dynamically adjusts which data points to retain or remove based on factors like spatial distribution, geometric importance, and computational resource availability. This dynamic approach allows the system to optimize the balance between representation quality and processing efficiency in real-time.
2Productivity
If the number of data points is reduced to improve system performance, then memory and processing requirements are reduced, but the representation quality and detail are compromised
Solution Approach 1:
The patent preserves high representation quality in critical regions by maintaining dense data sampling for edges, corners, and surface details, while applying aggressive decimation in uniform regions. This localized quality preservation ensures that system performance improvements from reduced data quantity do not compromise the accuracy of important geometric features.
Solution Approach 2:
The patent performs preliminary analysis of the point cloud data to identify and prioritize critical features before decimation occurs. By pre-determining which regions require preservation and which can be reduced, the system ensures that representation quality is maintained in essential areas while achieving the necessary reduction for improved system performance.
3Speed
If static decimation methods are used to reduce data points, then processing speed improves, but the ability to adapt to different data characteristics and preserve important features is limited
Solution Approach 1:
The patent implements dynamic decimation algorithms that adapt to the specific characteristics of each point cloud dataset. The system analyzes data properties such as spatial distribution, density variations, and geometric complexity to automatically adjust decimation strategies. This dynamic adaptation allows the system to optimize processing speed for each specific dataset while maintaining the ability to preserve important features based on its unique characteristics.
Solution Approach 2:
The patent incorporates feedback mechanisms that continuously monitor the effectiveness of decimation on representation quality and processing performance. Based on this feedback, the system iteratively adjusts decimation parameters and strategies to achieve optimal balance between speed and quality for each specific data characteristics, enabling adaptive optimization rather than static one-size-fits-all approaches.
Data Source
AI summary
An editing system may dynamically and intelligently determine which data points to remove, replace, and/or modify from a point cloud space so that more features, color information, and/or detail of the point cloud are preserved after decimation. The system may receive data points that are distributed in space, and may select one or more elements of the data points on which to base the decimation. For instance, the system may decimate a first subset of the data points by a first amount based on a first difference in values defined for the one or more elements of the first subset of data points, and may decimate a different second subset of the data points by a different second amount based on a second difference in values defined for the one or more elements of the second subset of data points.