3D Shape Filtering Pipeline for Reliable Generative AI Training
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Solution Overview
Problem
Conventional data curation systems for generating high-quality datasets require significant human resources and are prone to errors, leading to inaccurate and time-consuming data processing for machine learning models, especially in selecting, annotating, and aligning three-dimensional shapes.
Innovation Solution
Utilizing processing pipelines with trained machine learning models to automatically filter, align, and annotate three-dimensional shape data, reducing human intervention and enhancing accuracy by iteratively training models to determine quality scores, poses, and annotations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If human feedback is used to organize, annotate, and present data in conventional systems, then data quality can be maintained through manual review, but the process requires large amounts of human resources and time
Solution Approach 1:
The system enables automatic data curation where machine learning models self-evaluate and annotate 3D shape data without human intervention. The models process datasets, generate quality scores, create captions, and align poses autonomously, replacing manual human review while maintaining data quality through iterative training on curated subsets.
Solution Approach 2:
Manual human processes are replaced with automated machine learning pipelines. The system substitutes human cognitive tasks (quality assessment, annotation, alignment) with computational models that automatically process and curate 3D shape data, dramatically reducing time requirements while preserving reliability through model-based evaluation.
2Measurement precision
If human users manually select and annotate high-quality shapes, then accurate quality assessment can be achieved, but the process is prone to human error and requires significant human resources
Solution Approach 1:
Machine learning models perform self-assessment of 3D shape quality by automatically generating quality scores based on learned criteria. The models evaluate shapes, identify high-quality instances, and annotate them with captions and pose information autonomously, eliminating human error while managing complexity through automated iterative refinement on selected subsets.
Solution Approach 2:
The processing pipeline is segmented into specialized machine learning models, each handling specific tasks (quality scoring, caption generation, pose alignment). This modular approach manages complexity by dividing the overall curation process into discrete, trainable components that can be independently optimized and combined.
3Stability of the object's composition
If users manually set and select views for each shape in the final dataset, then consistent shape representation can be achieved, but the process is time-consuming and errors may be carried through the pipeline
Solution Approach 1:
Machine learning models automatically determine and align canonical poses for 3D shapes without manual intervention. The models consistently represent shapes by learning standard view configurations and applying them across the dataset, ensuring representation stability while dramatically increasing productivity through automated batch processing of shape alignments.
4Reliability
If conventional systems use human feedback for data curation, then high-quality datasets can be generated, but the process requires large amounts of human resources
Solution Approach 1:
The system replaces human resources with autonomous machine learning pipelines that perform complete data curation independently. Models automatically evaluate quality, generate annotations, and curate final datasets without human intervention, maintaining reliability through iterative training while eliminating the need for human resources in the curation process.
Solution Approach 2:
Human cognitive processes are substituted with computational machine learning systems. The patent replaces manual quality assessment, annotation, and curation tasks with automated models that process 3D shape data, generating high-quality datasets without requiring human resources while managing complexity through structured pipeline architecture.
Data Source
AI summary
In various examples, techniques for performing data filtering for AI systems and applications is described herein. Systems and methods described herein may use a pipeline that is configured to filter shapes, such as three-dimensional shapes, in order to identify high-quality shapes for a final dataset. In some examples, to identify the high-quality shapes, training shapes along with ground truth scores associated with the training shapes may be used to train a machine learning model. During and/or after the training, the machine learning model may then be used to process data associated with additional shapes in order to determine quality scores associated with the additional shapes. These quality scores may then be used to select the high-quality shapes, such as shapes that satisfy a threshold quality score. In some examples, additional filtering may be performed by the pipeline, such as by using one or more rules for removing low-quality shapes.