Qualcomm incorporated (20240320909). GENERATING SEMANTICALLY-LABELLED THREE-DIMENSIONAL MODELS simplified abstract
Contents
GENERATING SEMANTICALLY-LABELLED THREE-DIMENSIONAL MODELS
Organization Name
Inventor(s)
Michel Adib Sarkis of San Diego CA (US)
GENERATING SEMANTICALLY-LABELLED THREE-DIMENSIONAL MODELS - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240320909 titled 'GENERATING SEMANTICALLY-LABELLED THREE-DIMENSIONAL MODELS
Simplified Explanation: The patent application describes a method for generating three-dimensional models using machine learning and segmentation masks.
Key Features and Innovation:
- Obtaining images of an object and segmentation masks with labels.
- Training a machine-learning model to generate semantically-labeled 3D models.
- Generating a semantically-labeled 3D model of the object.
Potential Applications: This technology can be used in industries such as manufacturing, healthcare, and entertainment for creating accurate 3D models of objects.
Problems Solved: This technology addresses the need for efficient and accurate generation of 3D models from images.
Benefits:
- Improved accuracy in generating 3D models.
- Time-saving in the modeling process.
- Enhanced visualization of objects.
Commercial Applications: The technology can be applied in fields like virtual reality, augmented reality, and product design for creating realistic 3D models.
Prior Art: Readers can explore prior art related to image segmentation, machine learning in 3D modeling, and computer vision technologies.
Frequently Updated Research: Stay updated on advancements in machine learning algorithms for 3D modeling and image segmentation techniques.
Questions about 3D Modeling: 1. What are the key benefits of using machine learning in generating 3D models? 2. How does this technology improve the accuracy of 3D modeling processes?
Original Abstract Submitted
systems and techniques are described herein for generating one or more three-dimensional models. for instance, a method for generating one or more three-dimensional models is provided. the method may include obtaining a plurality of images of an object; obtaining a plurality of segmentation masks associated with the plurality of images, each segmentation mask of the plurality of segmentation masks including at least one label indicative of at least one segment of the object in a respective image of the plurality of images; training, using the plurality of images and the plurality of segmentation masks, a machine-learning model to generate one or more semantically-labeled three-dimensional models of the object; and generating using the trained machine-learning model, a semantically-labeled three-dimensional model of the object, the semantically-labeled three-dimensional model of the object including at least one label indicative of the at least one segment of the object.
