Google llc (20240161470). Machine-Learned Models for Implicit Object Representation simplified abstract
Contents
- 1 Machine-Learned Models for Implicit Object Representation
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 Machine-Learned Models for Implicit Object Representation - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.10 Original Abstract Submitted
Machine-Learned Models for Implicit Object Representation
Organization Name
Inventor(s)
Cristian Sminchisescu of Zürich (CH)
Thiemo Andreas Alldieck of Zürich (CH)
Machine-Learned Models for Implicit Object Representation - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240161470 titled 'Machine-Learned Models for Implicit Object Representation
Simplified Explanation
The present disclosure relates to a method for training a machine-learned model for implicit representation of an object, involving obtaining a latent code describing the shape of an object, determining spatial query points, processing the latent code and spatial query points with a machine-learned implicit object representation model to obtain implicit segment representations, determining an implicit object representation of the object and semantic data, evaluating a loss function, and adjusting parameters of the model based on the loss function.
- Obtaining latent code describing object shape
- Determining spatial query points
- Processing latent code and query points with machine-learned model for implicit object representation
- Determining implicit segment representations for object segments
- Evaluating loss function
- Adjusting model parameters based on loss function
Potential Applications
This technology could be applied in fields such as computer vision, robotics, and 3D modeling for tasks like object recognition, shape analysis, and scene understanding.
Problems Solved
This technology helps in efficiently representing objects with complex shapes and segments, enabling better understanding and manipulation of objects in various applications.
Benefits
The method allows for implicit representation of objects, which can lead to more accurate and detailed modeling of objects in a computationally efficient manner.
Potential Commercial Applications
One potential commercial application of this technology could be in the development of advanced computer vision systems for tasks like autonomous driving, object detection, and augmented reality.
Possible Prior Art
One possible prior art could be the use of machine learning models for object recognition and segmentation in computer vision applications. However, the specific method of training a machine-learned model for implicit object representation as described in this disclosure may be novel.
What are the limitations of the proposed method in terms of scalability and real-time processing?
The scalability of the method may be limited by the complexity of the object shapes and the size of the latent code. Real-time processing may also be a challenge depending on the computational resources required for training and inference.
How does this method compare to existing techniques for object representation and segmentation in terms of accuracy and efficiency?
This method may offer a more accurate representation of objects with complex shapes compared to traditional segmentation techniques. However, the efficiency of the method in terms of computational resources and processing time would need to be compared to existing techniques to evaluate its overall performance.
Original Abstract Submitted
systems and methods of the present disclosure are directed to a computer-implemented method for training a machine-learned model for implicit representation of an object. the method can include obtaining a latent code descriptive of a shape of an object comprising one or more object segments. the method can include determining spatial query points. the method can include processing the latent code and spatial query points with segment representation portions of a machine-learned implicit object representation model to obtain implicit segment representations for the object segments. the method can include determining an implicit object representation of the object and semantic data. the method can include evaluating a loss function. the method can include adjusting parameters of the machine-learned implicit object representation model based at least in part on the loss function.
