Distification Standardizes 3D Point Clouds for Predictive Models
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Solution Overview
Problem
The incompatibility and disparate nature of various image and video file formats used by different manufacturers create challenges for computer systems to analyze and coordinate 2D and 3D imagery, leading to difficulties in interoperability and accuracy in predictive models.
Innovation Solution
The Distification method transforms unstructured 3D imagery into a standardized format by generating a 2D image matrix with associated feature vectors, allowing for improved compatibility and accuracy in predictive models, even when used with existing systems like neural networks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple different image and video file formats are used by different manufacturers, then device compatibility and manufacturer independence are improved, but system interoperability and data coordination become difficult
Solution Approach 1:
The patent introduces a standardized intermediate data structure that acts as a mediator between different image and video formats. This intermediate structure enables uniform processing and analysis of diverse formats without requiring changes to the original formats or devices, thus maintaining manufacturer independence while improving system interoperability
Solution Approach 2:
The patent creates a universal data processing framework that can handle multiple image and video formats through a common interface. This universal approach allows the system to work with various formats (JPEG, PNG, PLY, etc.) using the same processing pipeline, eliminating the need for format-specific processing logic
2Measurement precision
If 3D imagery is stored with high granularity (tens of thousands of points), then image detail and precision are improved, but data processing complexity and computational requirements increase
Solution Approach 1:
The patent segments the high-granularity 3D point cloud data into multiple lower-granularity 2D image matrices. Each 2D matrix represents a portion of the 3D data at a coarser resolution, which can be processed independently and more efficiently while still capturing essential features for predictive analysis
Solution Approach 2:
The patent transforms 3D point cloud data into 2D image matrices, changing the dimensional representation to reduce processing complexity. This dimensionality reduction allows the system to work with simplified 2D representations that retain sufficient information for classification tasks while requiring fewer computational resources
3Adaptability or versatility
If 3D points are stored in unstructured sequences, then data storage flexibility is improved, but comparative analysis between different 3D images becomes difficult
Solution Approach 1:
The patent converts unstructured 3D point sequences into homogeneous 2D image matrix structures with consistent formatting. This standardization ensures that all 3D images are represented in the same uniform structure, enabling reliable comparative analysis while preserving the essential spatial information from the original unstructured data
Data Source
AI summary
Systems and methods are described for generating an image-based prediction model, where a computing device may obtain a set of 3D images from a 3D image data source. Each of the 3D images can have 3D point cloud data and a Distification technique can be applied to the 3D point cloud data of each 3D image to generate output feature vector(s). The output feature vector(s) may then be used to train and generate the image-based prediction model.