Point Cloud Octree Conversion via Preordered Traversal
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Solution Overview
Problem
Existing methods for converting point cloud datasets into a renderable format are inefficient, making it difficult to stream and render large datasets effectively, as they often require significant processing time and do not provide an optimal spatial organization.
Innovation Solution
The method involves reading header information to determine the bounding box and total data points, subdividing data into caches, and using a preordered traversal to create a hierarchical data structure, such as an octree, which allows for efficient storage and rendering by sampling and sorting data points into subtrees and tilesets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If existing methods are used to convert point cloud datasets into renderable format, then the conversion can be completed, but the processing time is excessive and rendering efficiency is poor
Solution Approach 1:
The patent segments the point cloud dataset into a hierarchical structure using octrees, dividing the data into manageable nodes at different levels of detail. This segmentation enables parallel processing and efficient memory management, significantly improving conversion speed and reducing processing time compared to handling the entire dataset as a single unit.
Solution Approach 2:
The patent introduces a hierarchical dimension to the point cloud data by organizing it into an octree structure with multiple levels. This dimensional organization allows the system to process and render data at appropriate levels of detail based on viewing distance and importance, dramatically improving rendering efficiency and enabling streaming of large datasets.
2Productivity
If point cloud data is organized into a hierarchical structure with multiple levels of detail, then rendering efficiency improves, but the data structure complexity increases
Solution Approach 1:
The patent implements a nested octree structure where each node contains child nodes, creating a hierarchical organization of point cloud data. This nesting allows the system to manage complexity by organizing data into manageable units at different scales, improving rendering efficiency while keeping the structure systematic and traversable through preordered traversal algorithms.
Data Source
AI summary
Methods and systems for converting a point cloud dataset into a hierarchical data structure for a lossless representation of an input file are provided herein. In various embodiments the method comprises recursing subtrees using a preordered traversal for a plurality of nodes, the preordered traversal being checking a parent node and then checking children nodes and stopping the recursing of the subtrees using the preordered traversal of the plurality of nodes when a node of the plurality of nodes is a below threshold node. The below threshold node being less than a configuration specified maximum number of data points, the below threshold node being a leaf of a tileset. Further comprising, aggregating the leaf of the tileset into a lossless representation of the collection of input files.