Intel corporation (20240161387). DEEP GEOMETRIC MODEL FITTING simplified abstract
Contents
- 1 DEEP GEOMETRIC MODEL FITTING
DEEP GEOMETRIC MODEL FITTING
Organization Name
Inventor(s)
Vladlen Koltun of Santa Clara CA (US)
DEEP GEOMETRIC MODEL FITTING - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240161387 titled 'DEEP GEOMETRIC MODEL FITTING
Simplified Explanation
The technology described in the abstract involves the use of neural networks to generate and update model weights based on input data, ultimately outputting a geometric model of the input data with reduced dependence on the number of data points.
- Neural networks used to generate and update model weights
- Output a geometric model of input data
- Reduced dependence on the number of data points in input data
Potential Applications
This technology could be applied in various fields such as computer vision, image processing, and pattern recognition where geometric modeling of data is required.
Problems Solved
This technology addresses the challenge of efficiently generating geometric models from input data without being overly reliant on the quantity of data points.
Benefits
The benefits of this technology include improved efficiency in generating geometric models, reduced computational resources required, and potentially more accurate modeling results.
Potential Commercial Applications
With its ability to generate geometric models with reduced data dependency, this technology could be valuable in industries such as manufacturing, healthcare imaging, and autonomous vehicles for improved decision-making processes.
Possible Prior Art
One possible prior art could be the use of traditional machine learning algorithms for geometric modeling, which may not be as efficient or effective as the neural network-based approach described in this technology.
Unanswered Questions
How does this technology compare to existing methods for geometric modeling?
This article does not provide a direct comparison to existing methods for geometric modeling, leaving the reader to wonder about the specific advantages and limitations of this technology in relation to traditional approaches.
What are the potential limitations or challenges of implementing this technology in real-world applications?
The article does not address the potential obstacles or difficulties that may arise when implementing this technology in practical settings, leaving room for speculation on the feasibility and scalability of the proposed approach.
Original Abstract Submitted
systems, apparatuses and methods may provide for technology that generates, by a first neural network, an initial set of model weights based on input data and iteratively generates, by a second neural network, an updated set of model weights based on residual data associated with the initial set of model weights and the input data. additionally, the technology may output a geometric model of the input data based on the updated set of model weights. in one example, the first neural network and the second neural network reduce the dependence of the geometric model on the number of data points in the input data.
