Point Cloud Alignment Using ML Correspondence Scoring
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Solution Overview
Problem
Existing additive manufacturing techniques face challenges in achieving sub-millimeter resolution and efficiency in predicting or inferring the geometry of manufactured objects, with first principle-based methods being slow and machine learning approaches offering improved but still limited results.
Innovation Solution
A machine learning model, utilizing a deep neural network, aligns 3D object models by normalizing point clouds, determining edge features, and calculating correspondence scores to achieve precise alignment and deformation compensation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If first principle-based methods are used to predict or infer the geometry of manufactured objects, then accuracy is improved, but computation speed deteriorates
Solution Approach 1:
The system performs preliminary actions by pre-processing point cloud data, normalizing coordinates, and preparing training datasets before the actual geometry prediction task. This includes extracting features from point clouds and organizing them in advance, which accelerates the subsequent prediction process while maintaining accuracy
Solution Approach 2:
The patent replaces traditional first principle-based mechanical computation methods with machine learning models that learn geometric relationships from data. This substitution enables faster prediction speeds while maintaining the accuracy needed for manufacturing applications
2Speed
If machine learning approaches are used to predict or infer the geometry of manufactured objects, then computation speed is improved, but measurement precision deteriorates
Solution Approach 1:
The system changes parameters by normalizing point cloud coordinates to a standard scale, adjusting feature extraction parameters, and optimizing model hyperparameters. This enables the machine learning model to achieve both fast computation and high precision by operating in an optimized parameter space
Solution Approach 2:
The patent employs a composite approach by combining multiple machine learning techniques and processing stages. This includes integrating point cloud normalization, feature extraction, and prediction models into a unified system that leverages the strengths of each component to achieve both speed and precision
3Manufacturing precision
If point cloud alignment is performed with high precision, then manufacturing precision is improved, but device complexity increases
Solution Approach 1:
The alignment process is segmented into distinct stages: point cloud normalization, feature extraction, correspondence matching, and transformation calculation. This segmentation reduces complexity by breaking down the complex alignment task into manageable, independent steps that can be processed sequentially
Solution Approach 2:
The system performs self-service by automatically normalizing point cloud data and extracting features without requiring manual intervention. This automation maintains high alignment precision while reducing the operational complexity for users
Data Source
AI summary
Examples of methods for point cloud alignment are described herein. In some examples, a method includes orienting a model point cloud or a scanned point cloud based on a set of initial orientations. In some examples, the method includes determining, using a first portion of a machine learning model, first features of the model point cloud and second features of the scanned point cloud. In some examples, the method includes determining, using a second portion of the machine learning model, correspondence scores between the first features and the second features based on the set of initial orientations. In some examples, the method includes globally aligning the model point cloud and the scanned point cloud based on the correspondence scores.