20230069019. REALITY MODEL OBJECT RECOGNITION USING CROSS-SECTIONS simplified abstract (SKYYFISH LLC)
REALITY MODEL OBJECT RECOGNITION USING CROSS-SECTIONS
Organization Name
Inventor(s)
Orest Jacob Pilskalns of Missoula MT (US)
REALITY MODEL OBJECT RECOGNITION USING CROSS-SECTIONS - A simplified explanation of the abstract
This abstract first appeared for US patent application 20230069019 titled 'REALITY MODEL OBJECT RECOGNITION USING CROSS-SECTIONS
Simplified Explanation
The abstract describes a model object recognition system that uses photogrammetry to capture multiple views of a 3D object. These views are then converted into 2D cross-sections at different elevations and angles. The relationship between these cross-sections is crucial for identification purposes. The system utilizes these cross-sections to automatically recognize and identify real-world equipment mounted on the 3D object, as well as detect any anomalies in the equipment. This allows for prompt remedial action if necessary.
- The system uses photogrammetry to capture various views of a 3D object.
- These views are converted into 2D cross-sections at different elevations and angles.
- The relationship between these cross-sections is important for identification.
- The system automatically recognizes and identifies real-world equipment mounted on the 3D object.
- It also detects any anomalies in the equipment.
- Remedial action can be ordered promptly if needed.
Potential Applications
This technology has potential applications in various industries, including:
- Manufacturing: It can be used to identify and locate equipment on 3D models of machinery, allowing for efficient maintenance and troubleshooting.
- Construction: The system can help identify and locate specific components or equipment on architectural models, aiding in project planning and coordination.
- Archaeology: It can assist in the identification and analysis of artifacts by creating accurate 3D models and cross-sections for further study.
- Medical Imaging: The technology can be utilized to identify and locate abnormalities or anomalies in medical scans, improving diagnostic accuracy.
Problems Solved
The model object recognition system addresses the following problems:
- Manual identification and location of equipment on 3D models can be time-consuming and prone to errors.
- Detecting anomalies or abnormalities in equipment may require extensive manual inspection.
- Prompt remedial action may be delayed if equipment issues are not identified in a timely manner.
Benefits
The use of this technology offers several benefits:
- Automation: The system automates the process of equipment recognition and identification, saving time and reducing human error.
- Accuracy: By utilizing multiple views and cross-sections, the system provides a more comprehensive and accurate analysis of the equipment.
- Efficiency: The technology enables prompt detection of anomalies, allowing for timely remedial action and minimizing potential downtime.
- Cost-effective: By streamlining the identification and location process, the system helps optimize maintenance and repair efforts, reducing overall costs.
Original Abstract Submitted
a reality-based model object recognition system using cross-sections includes using photogrammetry to obtain various views of a 3-dimensional (3d) object (e.g. 3d model, 3d reality model, mesh, etc.). the process then generates 2-dimensional (2d) slices, i.e. cross-sections, of the 3d object at various elevations and angles. the relation between the slices is critical for identification. the 2d slices are used as building blocks for automatic recognition and identification and location (e.g. x,y,z+angle) of a real-world equipment mounted on the 3d object and identifying any anomaly in the equipment so that remedial action may be ordered, if needed.
