Multi-Tree Point Cloud Processing for Rendering Performance
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Solution Overview
Problem
Point clouds, which provide accurate 3D representations with high resolution, result in significant computational and resource overhead due to the large volume of data points, leading to decreased performance in processing and rendering.
Innovation Solution
The system partitions a point cloud into multiple trees based on differentiation criteria, allowing for sequential or parallel processing of specific subsets of data points, reducing the need to access and process all data points simultaneously, thereby optimizing resource usage and improving responsiveness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If point cloud data points are processed together to maintain high resolution and accuracy, then measurement precision is improved, but productivity deteriorates due to the large volume of data requiring simultaneous processing
Solution Approach 1:
The point cloud data is divided into multiple octants based on spatial coordinates, with each octant processed independently. This segmentation allows the system to handle only the relevant portion of data points needed for the current rendering task, rather than processing all millions or billions of points simultaneously, thus improving processing speed while maintaining accuracy for the visible region.
2Measurement precision
If all point cloud data points are loaded into memory to ensure complete 3D representation, then measurement precision is improved, but device complexity increases due to memory and computational resource requirements
Solution Approach 1:
The system extracts and processes only the necessary subset of data points from the complete point cloud. By using octant-based spatial partitioning and frustum culling, the system identifies and loads only the data points that contribute to the current view, extracting the essential information needed for rendering while discarding or deferring loading of irrelevant data, thus reducing memory overhead.
3Adaptability or versatility
If modifications are made to point cloud data points, then adaptability is improved, but productivity deteriorates because changes require cascading processing of large volumes of other data points
Solution Approach 1:
The octant-based segmentation isolates data points into independent spatial regions. When a modification is needed, the system can apply changes to specific octants or subsets of points without triggering cascading processing across the entire point cloud. This localized processing approach maintains adaptability for data manipulation while preserving rendering performance by limiting the scope of required updates.
Data Source
AI summary
Disclosed is a system for efficiently accessing a point cloud via a multi-tree deconstruction of the point cloud. The system may receive the point cloud, may differentiate different sets of data points from the point cloud using differentiation criteria, and may generate different trees with each tree having leaf nodes corresponding to one of the differentiated sets of data points and parent nodes defined according to commonality in values of two or more leaf nodes. The system may receive a request to render the 3D environment, load a first tree into memory, generate a first image from the first tree data points, flush the first tree from the memory, load a second tree into the memory, generate a second image from the second tree data points, and present a composite image by combining at least the first image with the second image.