Point Cloud Editing via Bounding Volume Hierarchies
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Solution Overview
Problem
Editing, rendering, and processing large point clouds with millions or billions of data points are resource-intensive and time-consuming due to the vast amounts of data, necessitating conversion to lower resolution formats for efficient handling.
Innovation Solution
Converting point clouds into bounding volume hierarchies (BVHs) for efficient editing, animation, and processing, allowing for selective modification and traversal of data points within desired regions, reducing the need to scan all data points and minimizing resource usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If point clouds are converted to lower resolution formats for efficient handling, then processing speed and resource usage improve, but data precision and detail are lost
Solution Approach 1:
The point cloud data is segmented into a hierarchical structure using bounding volume hierarchies (BVHs), where the data is divided into multiple levels of spatial regions. This segmentation allows the system to process only the relevant portions of the point cloud at each resolution level, improving processing speed without losing overall data precision by maintaining the original high-resolution data in the detailed levels of the hierarchy.
2Reliability
If all data points in a point cloud are scanned for editing and processing, then complete data coverage is achieved, but processing time and resource consumption increase
Solution Approach 1:
The system performs preliminary organization of point cloud data into a bounding volume hierarchy structure before editing and processing operations. This preliminary action creates an indexed spatial framework that enables rapid location and access to specific data regions, allowing the system to process only the necessary portions of the point cloud while maintaining complete data coverage when needed, thereby reducing processing time without sacrificing reliability.
3Measurement precision
If high-resolution point cloud formats are used for editing and animation, then data detail is preserved, but resource consumption and processing complexity increase
Solution Approach 1:
The patent introduces a hierarchical dimension to the point cloud data structure by organizing data into multiple levels of bounding volume hierarchies. This dimensional transformation allows the system to work with aggregated, lower-complexity representations at higher levels of the hierarchy while preserving access to detailed high-resolution data at lower levels, thereby reducing processing complexity without sacrificing data detail when needed.
Data Source
AI summary
Disclosed is a system that receives a point cloud, and that generates a Bounding Volume Hierarchy (“BVH”) based on the point cloud data points. The BVH includes leaf nodes and parent nodes at one or more levels above the leaf nodes. The leaf nodes correspond to the point cloud data points. The system may receive input for adjusting a first set of elements of data points that are identified based on values specified for a second set of elements, and may locate those data points by traversing the BVH to arrive at a particular parent node that encompasses the values specified for the second set of elements. The system may then modify, based on the input, the first set of elements of a set of data points that correspond to a set of leaf nodes from the BVH that are directly or indirectly linked to the particular parent node.