Microsoft technology licensing, llc (20240135576). Three-Dimensional Object Detection simplified abstract
Contents
- 1 Three-Dimensional Object Detection
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 Three-Dimensional Object Detection - 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
Three-Dimensional Object Detection
Organization Name
microsoft technology licensing, llc
Inventor(s)
Zheng Zhang of San Carlos CA (US)
Three-Dimensional Object Detection - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240135576 titled 'Three-Dimensional Object Detection
Simplified Explanation
The patent application proposes a solution for three-dimensional (3D) object detection by extracting feature representations from point cloud data related to a 3D object and generating a detection result based on correlations between candidate 3D objects and the points.
- Feature representations are extracted from point cloud data related to a 3D object.
- Initial feature representations of candidate 3D objects are determined based on the feature representations of the points.
- Detection result for the 3D object is generated by determining self-correlations between candidate 3D objects and cross-correlations between the points and candidate 3D objects.
Potential Applications
The technology can be applied in various fields such as autonomous driving, robotics, augmented reality, and virtual reality for accurate and efficient 3D object detection.
Problems Solved
1. Accurate localization and recognition of 3D objects in a 3D scene without grouping points into candidate objects. 2. Efficient and reliable detection of objects in complex environments.
Benefits
1. Improved accuracy in 3D object detection. 2. Faster processing of 3D scene data. 3. Enhanced performance in real-world applications.
Potential Commercial Applications
"Enhancing 3D Object Detection in Autonomous Vehicles and Robotics"
Possible Prior Art
Prior art in 3D object detection includes methods based on deep learning algorithms, LiDAR technology, and computer vision techniques.
Unanswered Questions
How does this technology compare to existing 3D object detection methods in terms of accuracy and efficiency?
The article does not provide a direct comparison with existing methods in terms of accuracy and efficiency. Further research or testing may be needed to evaluate the performance of this technology against other approaches.
What are the potential limitations or challenges of implementing this technology in real-world scenarios?
The article does not address potential limitations or challenges of implementing this technology in real-world scenarios. Factors such as computational resources, environmental conditions, and scalability could impact the practicality of deploying this solution.
Original Abstract Submitted
according to implementations of the subject matter described herein, a solution is proposed for three-dimensional (3d) object detection. in this solution, feature representations of a plurality of points are extracted from point cloud data related to a 3d object. initial feature representations of a set of candidate 3d objects are determined based on the feature representations of the plurality of points. based on the feature representations of the plurality of points and the initial feature representations of the set of candidate 3d objects, a detection result for the 3d object is generated by determining self-correlations between the set of candidate 3d objects and cross-correlations between the plurality of points and the set of candidate 3d objects. in this way, without grouping points into candidate 3d objects, the 3d object in a 3d scene can be localized and recognized based on the self-correlations and cross-correlations.
