Object Ingestion Engine for Automated Database Population
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Solution Overview
Problem
Existing object recognition systems face challenges in efficiently populating databases with large numbers of ordinary or commodity objects, especially in uncontrolled settings, due to the need for extensive image data from various views and the difficulty in automating the ingestion process.
Innovation Solution
The system employs a canonical shape database and an object ingestion engine to autonomously ingest real-world objects into an object recognition database. The engine derives edges from image data, matches them with shape objects in the database, and generates key frame bundles for object recognition, allowing for efficient database construction and update in uncontrolled settings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional object recognition systems image and analyze objects from many different views in a controlled setting, then recognition accuracy is improved, but the ingestion process becomes time-consuming and expensive
Solution Approach 1:
The system performs preliminary actions by pre-computing and storing key frame bundles with extracted features, descriptors, and geometric information during the ingestion phase. This allows the recognition system to use these pre-prepared data structures directly during runtime, eliminating the need to process multiple views from scratch and significantly reducing recognition time while maintaining accuracy.
Solution Approach 2:
The system creates simplified copies of objects in the form of key frame bundles that contain essential recognition information (features, descriptors, geometric data) without storing complete multi-view image datasets. These compact representations serve as efficient substitutes for the original complex multi-view data, enabling fast comparison and matching during recognition while preserving the necessary information for accurate identification.
2Measurement precision
If complete multi-view image data is stored for each object, then recognition accuracy is improved, but database size and storage requirements increase
Solution Approach 1:
The system extracts only the essential recognition information from complete multi-view image data, storing key frame bundles that contain selected features, descriptors, and geometric information rather than the full image datasets. This extraction process removes redundant data while retaining the critical elements needed for accurate object recognition, significantly reducing database size.
Solution Approach 2:
The system segments the complete object representation into multiple key frame bundles, each containing specific features and descriptors from different views. Instead of storing monolithic multi-view datasets, the information is divided into discrete, manageable segments that can be independently processed and stored, reducing overall storage requirements while maintaining comprehensive recognition capability.
3Reliability
If manual object ingestion processes are used, then database quality is improved, but productivity and throughput decrease
Solution Approach 1:
The system implements self-service automation where the object ingestion process autonomously captures images, extracts features, generates key frame bundles, and populates the database without requiring manual intervention for each object. The automated pipeline maintains data quality through consistent feature extraction and validation procedures while dramatically increasing ingestion throughput compared to manual processes.
Solution Approach 2:
The system replaces manual mechanical processes with automated computational methods. Instead of human operators manually capturing and processing object images, the system uses automated image capture devices, computer vision algorithms for feature extraction, and computational pipelines for generating key frame bundles, thereby maintaining quality while significantly improving productivity.
4Measurement precision
If detailed feature extraction is performed on all objects, then recognition accuracy is improved, but computational complexity and processing time increase
Solution Approach 1:
The system performs partial feature extraction by focusing on key frames and essential features rather than exhaustively processing all possible views and features of each object. By selecting representative key frames and extracting only the most discriminative features, the system achieves sufficient recognition accuracy while significantly reducing computational complexity and processing time.
Data Source
AI summary
An object recognition ingestion system is presented. The object ingestion system captures image data of objects, possibly in an uncontrolled setting. The image data is analyzed to determine if one or more a priori know canonical shape objects match the object represented in the image data. The canonical shape object also includes one or more reference PoVs indicating perspectives from which to analyze objects having the corresponding shape. An object ingestion engine combines the canonical shape object along with the image data to create a model of the object. The engine generates a desirable set of model PoVs from the reference PoVs, and then generates recognition descriptors from each of the model PoVs. The descriptors, image data, model PoVs, or other contextually relevant information are combined into key frame bundles having sufficient information to allow other computing devices to recognize the object at a later time.