3D Distification Method for Point Cloud Standardization
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The complexity and incompatibility of various image and video file formats pose challenges for computer systems in analyzing and interoperating 3D imagery, particularly due to differences in data structures and sequencing of 3D points, which affects predictive modeling accuracy and classification.
Innovation Solution
The Distification method transforms unstructured 3D imagery into a standardized format by generating a 2D image matrix from 3D point clouds, creating output feature vectors that include depth coordinates and distance values, enabling improved compatibility and accuracy in predictive models, even when used with existing neural networks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If 3D imagery is stored in detailed formats like PLY with thousands of points, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent segments 3D point cloud data into multiple 2D image matrices by projecting points onto different viewing planes. Each 2D matrix contains a subset of 3D points with their projected coordinates, effectively dividing the complex 3D data structure into manageable 2D components that are easier to process while preserving the essential spatial information.
Solution Approach 2:
The patent transforms 3D point cloud data into 2D image matrices by projecting three-dimensional points onto two-dimensional planes. This dimensionality reduction simplifies the data structure from thousands of 3D points to organized 2D grids, making the data more compatible with standard image processing algorithms while retaining sufficient information for predictive modeling.
2Adaptability or versatility
If different manufacturers use different image and video formats, then adaptability is improved, but ease of operation worsens
Solution Approach 1:
The patent creates a universal processing framework that can handle multiple 3D image formats (PLY, PCD, and other point cloud formats) by converting them all into a standardized 2D image matrix representation. This universal approach allows the system to process data from different manufacturers regardless of their native format, eliminating interoperability issues while maintaining support for diverse input types.
Solution Approach 2:
The patent introduces 2D image matrices as an intermediary representation between different 3D point cloud formats and the predictive modeling system. This intermediate format serves as a common language that translates various manufacturer-specific formats into a unified structure, facilitating seamless data exchange and processing across different systems without requiring format-specific handling.
3Adaptability or versatility
If 3D points are sequenced in unstructured orders, then adaptability is improved, but measurement precision worsens
Solution Approach 1:
The patent applies preliminary ordering to 3D points based on their spatial coordinates before creating 2D image matrices. By pre-sorting and organizing points according to their x, y, z coordinates and establishing a consistent sequencing rule, the system ensures that corresponding points across different 3D images follow the same order. This preliminary organization maintains adaptability to handle various input formats while significantly improving measurement precision for predictive modeling.
Data Source
AI summary
Systems and methods are described for Distification of 3D imagery. A computing device may obtain a three dimensional (3D) image that defines a 3D point cloud used to generate a two dimensional (2D) image matrix. The 2D image matrix may include 2D matrix point(s), where each 2D matrix point can be associated with a horizontal coordinate and a vertical coordinate. The computing device can generate an output feature vector that includes at least one 2D matrix point of the 2D image matrix, and a 3D point in the 3D point cloud of the 3D image. The 3D point in the 3D point cloud is mapped to a coordinate pair comprised of the horizontal coordinate and the vertical coordinate of the at least one 2D matrix point of the 2D image matrix point. The output feature vector is input into a predictive model.