18274679. Three-Dimensional Object Detection simplified abstract (Microsoft Technology Licensing, LLC)
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 18274679 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 self-correlations and cross-correlations.
- Feature representations extracted from point cloud data
- Determination of initial feature representations for candidate 3D objects
- Generation of detection result based on self-correlations and cross-correlations
Potential Applications
This technology can be applied in various fields such as autonomous driving, robotics, augmented reality, and virtual reality for accurate and efficient 3D object detection and recognition.
Problems Solved
1. Accurate localization and recognition of 3D objects in a scene without grouping points into candidate objects. 2. Efficient processing of point cloud data for object detection in complex environments.
Benefits
1. Improved accuracy in 3D object detection. 2. Reduced computational complexity compared to traditional methods. 3. Enhanced performance in real-time applications.
Potential Commercial Applications
Enhanced 3D object detection technology for industries such as automotive, surveillance, gaming, and industrial automation.
Possible Prior Art
One possible prior art could be the use of machine learning algorithms for 3D object detection in point cloud data. However, the specific approach of generating detection results based on self-correlations and cross-correlations as described in this patent application may be a novel innovation.
Unanswered Questions
How does this technology compare to existing 3D object detection methods?
This article does not provide a direct comparison with existing 3D object detection methods in terms of accuracy, efficiency, and computational complexity.
What are the potential limitations or challenges of implementing this technology in real-world applications?
The article does not address the potential limitations or challenges that may arise when implementing this technology in real-world scenarios, such as scalability, robustness in varying environmental conditions, and integration with existing systems.
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.
