3D Shape Annotation Pipeline for Faster AI Training Data Curation
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Solution Overview
Problem
Conventional data curation systems for generating high-quality datasets for machine learning models require significant human resources and are prone to errors, leading to inaccurate and time-consuming data processing.
Innovation Solution
Utilizing processing pipelines that include filtering, alignment, and annotation pipelines to automatically curate and annotate 2D and 3D data, employing trained machine learning models to determine quality scores, poses, and generate captions, reducing human intervention and enhancing accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If human feedback is used to curate and annotate data, then data quality can be maintained, but the process requires large amounts of human resources and time
Solution Approach 1:
The system uses trained machine learning models to automatically perform data curation and annotation tasks that were previously requiring human feedback. The models evaluate quality scores, determine poses, and generate captions autonomously, enabling the system to serve itself without extensive human intervention while maintaining consistent data quality standards
Solution Approach 2:
The patent replaces the mechanical human labor process with an automated computational system. Machine learning models substitute for human annotators in evaluating shape quality, determining canonical poses, and generating text captions, thereby eliminating the need for manual processing while maintaining or improving consistency and speed
2Ease of operation
If human feedback is used to curate and annotate data, then data can be processed, but the process is prone to human error
Solution Approach 1:
The automated pipeline performs data curation and annotation without human intervention, eliminating the source of human error. The machine learning models consistently apply evaluation criteria for quality scores, pose determination, and caption generation, ensuring uniform processing across all data points without the variability inherent in manual operations
Solution Approach 2:
The system incorporates quality evaluation feedback loops where trained models assess data quality scores and automatically filter or correct substandard entries. This automated feedback mechanism ensures consistent quality control without relying on human judgment, reducing errors while maintaining operational simplicity through systematic validation
3Reliability
If users manually select and annotate shapes, then high-quality datasets can be generated, but the process is time-consuming
Solution Approach 1:
The system performs preliminary automated processing including quality score evaluation, pose determination, and caption generation before final dataset compilation. By pre-processing data through trained machine learning models, the system prepares high-quality annotated datasets much faster than manual processes while maintaining consistent quality standards across all entries
Solution Approach 2:
The automated pipeline independently completes the entire workflow from raw shape data to annotated dataset without requiring manual selection or annotation steps. The machine learning models self-evaluate quality, self-determine poses, and self-generate captions, dramatically reducing processing time while ensuring reliable dataset quality through consistent automated application of evaluation criteria
4Productivity
If automated pipelines are used, then processing speed improves, but the system complexity increases
Solution Approach 1:
The automated system is divided into distinct processing pipelines: quality evaluation pipeline, pose determination pipeline, and caption generation pipeline. Each pipeline is independently trained and optimized for its specific task, allowing parallel processing that improves speed while managing complexity through modular design. The segmentation enables each component to be developed and maintained separately
5Productivity
If automated pipelines are used, then human resources are reduced, but the automation level increases
Solution Approach 1:
The system achieves high-level automation where machine learning models autonomously perform quality evaluation, pose determination, and caption generation without human intervention. The automated pipelines process data end-to-end, dramatically increasing throughput while minimizing human resource requirements. The models self-manage the entire workflow from input shape data to output annotated datasets
Data Source
AI summary
In various examples, techniques for automatic annotation of shapes for AI systems and applications is described herein. Systems and methods described herein may use a pipeline that is configured to generate annotations for shapes, such as three-dimensional shapes, using various types of captions. For instance, image data representing images of the shapes, data representing description of the shapes, and/or data representing a format for the annotations may be input into one or more multimodal language models. The multimodal language model(s) may then be configured to process the data and, based at least on the processing, generate short captions and long captions associated with the shapes. These captions may then be stored in association with the shapes and/or the images. In some examples, embeddings may initially be generated for the captions, where the embeddings are then stored in association with the shapes and/or the images.