Apple inc. (20240161527). Camera Parameter Estimation Using Semantic Labels simplified abstract
Contents
- 1 Camera Parameter Estimation Using Semantic Labels
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 Camera Parameter Estimation Using Semantic Labels - 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
Camera Parameter Estimation Using Semantic Labels
Organization Name
Inventor(s)
Payal Jotwani of Santa Clara CA (US)
Camera Parameter Estimation Using Semantic Labels - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240161527 titled 'Camera Parameter Estimation Using Semantic Labels
Simplified Explanation
The patent application describes a device that can analyze a scene by obtaining a point cloud and a two-dimensional image, then correlating the two to identify objects in the scene.
- The device obtains a point cloud of a scene with three-dimensional coordinates.
- It captures a two-dimensional image of the scene using a camera.
- The device detects objects in the two-dimensional image and matches them with points in the point cloud.
- It estimates the camera's intrinsic parameters based on the correlation between the two sets of coordinates.
Potential Applications
This technology could be applied in various fields such as augmented reality, autonomous driving, robotics, and industrial automation for object recognition and scene understanding.
Problems Solved
This technology solves the problem of accurately mapping objects in a scene from a point cloud to a two-dimensional image, improving object detection and localization.
Benefits
The benefits of this technology include enhanced object recognition, improved accuracy in spatial mapping, and better understanding of complex scenes.
Potential Commercial Applications
Potential commercial applications of this technology include autonomous vehicles, surveillance systems, virtual reality applications, and industrial inspection processes.
Possible Prior Art
One possible prior art for this technology could be the use of point cloud data in conjunction with images for object recognition and scene understanding in computer vision research.
Unanswered Questions
How does this technology handle occlusions in the scene?
The patent application does not specify how the device deals with occlusions when correlating the point cloud with the two-dimensional image. This could be a crucial aspect to consider in real-world applications where objects may partially block each other in the scene.
What is the computational complexity of the algorithm used in this technology?
The patent application does not provide information on the computational complexity of the algorithm used to correlate the point cloud with the two-dimensional image. Understanding the computational requirements of the technology is essential for assessing its feasibility in real-time applications.
Original Abstract Submitted
a device obtains a point cloud of a scene including a plurality of points. each point has three-dimensional coordinates in a three-dimensional coordinate system. a first cluster of points has a first semantic label. the device obtains a two-dimensional image of the scene with a camera with an intrinsic parameter. the device detects, in the two-dimensional image, a representation of a first object corresponding to the first semantic label. the device determines two-dimensional coordinates in a two-dimensional coordinate system of the two-dimensional image corresponding to the first object. the device determines, from the first cluster of points, three-dimensional coordinates in the three-dimensional coordinate system of the scene corresponding to the two-dimensional coordinates in the two-dimensional coordinate system of the two-dimensional image of the scene. the device estimates the intrinsic parameter based on the two-dimensional and the three-dimensional coordinates.
