Mixed Reality Object Detection Using Point Cloud Partitioning
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Solution Overview
Problem
Existing mixed reality rendering engines require substantial memory and GPU resources due to complex layering operations and fail to enable effective segmentation based on image data, particularly in delicate settings like medical applications, leading to inefficient resource utilization and inaccurate modeling of physical environments.
Innovation Solution
The proposed solution employs geometrically-aware object detection using point cloud partitions, which reduces memory and GPU requirements by enabling accurate segmentation based on depth data and direct object mapping, thereby optimizing resource usage and improving rendering efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If complex layering operations are used in mixed reality rendering, then rendering accuracy is improved, but memory and GPU resource requirements increase substantially
Solution Approach 1:
The patent segments the point cloud data into multiple depth-based partitions, processing each partition separately rather than handling the entire scene as a single complex structure. This segmentation reduces the computational burden on memory and GPU resources while maintaining rendering accuracy through systematic processing of divided data sets.
Solution Approach 2:
The patent introduces depth-based partitioning as an additional dimensional approach to organize and process point cloud data. By sorting points according to their depth values and creating structured partitions along the depth dimension, the system achieves efficient resource utilization while preserving rendering fidelity.
2Productivity
If depth-based partitioning and object-based partitioning are implemented, then resource utilization is optimized, but processing complexity increases
Solution Approach 1:
The patent implements a two-stage partitioning system where point cloud data is first divided into depth-based partitions, then further organized into object-based partitions. This hierarchical segmentation approach optimizes resource utilization by enabling targeted processing of specific regions and objects, while the systematic structure manages processing complexity through organized data flow.
Solution Approach 2:
The patent performs preliminary depth-based sorting and partitioning of point cloud data before subsequent object detection and rendering operations. This preliminary organization of data into depth-stratified partitions reduces the complexity of later processing stages by pre-establishing an efficient data structure that facilitates faster object-based partitioning and rendering.
3Measurement precision
If point cloud partitions are used for geometrically-aware object detection, then segmentation accuracy is improved, but computational requirements increase
Solution Approach 1:
The patent divides the point cloud into depth-based partitions and further into object-based partitions, enabling geometrically-aware object detection to operate on smaller, more manageable data subsets. This segmentation improves segmentation accuracy by allowing detailed geometric analysis of specific regions while reducing computational requirements compared to processing the entire point cloud simultaneously.
Solution Approach 2:
The patent applies different processing strategies to different depth-based partitions and object-based partitions, tailoring the geometrically-aware object detection to local characteristics of each partition. This local quality approach improves segmentation accuracy for specific objects while optimizing computational resource allocation, as each partition receives processing appropriate to its specific geometric properties rather than uniform processing of all data.
Data Source
AI summary
In general, embodiments of the present invention provide methods, apparatuses, systems, computing devices, computing entities, and/or the like for performing mixed reality processing using at least one of depth-based partitioning of a point cloud capture data object, object-based partitioning of a point cloud capture data object, mapping a partitioned point cloud capture data object to detected objects of a three-dimensional scan data object, performing noise filtering on point cloud capture data objects based at least in part on geometric inferences from three-dimensional scan data objects, and performing geometrically-aware object detection using point cloud capture data objects based at least in part on geometric inferences from three-dimensional scan data objects.