18286059. METHOD AND APPARATUS OF ENCODING/DECODING POINT CLOUD GEOMETRY DATA CAPTURED BY A SPINNING SENSORS HEAD simplified abstract (BEIJING XIAOMI MOBILE SOFTWARE CO., LTD.)
Contents
- 1 METHOD AND APPARATUS OF ENCODING/DECODING POINT CLOUD GEOMETRY DATA CAPTURED BY A SPINNING SENSORS HEAD
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 METHOD AND APPARATUS OF ENCODING/DECODING POINT CLOUD GEOMETRY DATA CAPTURED BY A SPINNING SENSORS HEAD - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Key Features and Innovation
- 1.6 Potential Applications
- 1.7 Problems Solved
- 1.8 Benefits
- 1.9 Commercial Applications
- 1.10 Prior Art
- 1.11 Frequently Updated Research
- 1.12 Questions about Point Cloud Encoding/Decoding
- 1.13 Original Abstract Submitted
METHOD AND APPARATUS OF ENCODING/DECODING POINT CLOUD GEOMETRY DATA CAPTURED BY A SPINNING SENSORS HEAD
Organization Name
BEIJING XIAOMI MOBILE SOFTWARE CO., LTD.
Inventor(s)
Jonathan Taquet of Beijing (CN)
Sebastien Lasserre of Beijing (CN)
METHOD AND APPARATUS OF ENCODING/DECODING POINT CLOUD GEOMETRY DATA CAPTURED BY A SPINNING SENSORS HEAD - A simplified explanation of the abstract
This abstract first appeared for US patent application 18286059 titled 'METHOD AND APPARATUS OF ENCODING/DECODING POINT CLOUD GEOMETRY DATA CAPTURED BY A SPINNING SENSORS HEAD
Simplified Explanation
The patent application describes a method for encoding and decoding point cloud data representing a physical object by using prediction data to derive candidate predictors for encoding geometry data of the points.
- Dynamic list of prediction data is determined for encoding points.
- Prediction data is updated based on residuals radius of decoded points during encoding.
- Candidate predictors are derived from prediction data for encoding geometry data of points.
Key Features and Innovation
- Encoding and decoding point cloud data using prediction data.
- Dynamic list of prediction data for efficient encoding.
- Updating prediction data based on residuals radius for accurate encoding.
- Deriving candidate predictors from prediction data for encoding geometry data.
Potential Applications
The technology can be applied in various fields such as:
- 3D modeling and rendering
- Virtual reality and augmented reality
- Autonomous driving systems
- Robotics and automation
Problems Solved
- Efficient encoding and decoding of point cloud data.
- Improved accuracy in encoding geometry data.
- Dynamic prediction data for better encoding results.
Benefits
- Enhanced data compression for point cloud data.
- Improved accuracy in representing physical objects.
- Efficient utilization of prediction data for encoding.
Commercial Applications
- The technology can be used in industries such as:
- Computer graphics and animation
- Geospatial mapping and surveying
- Medical imaging and diagnostics
- Industrial automation and quality control
Prior Art
Information on prior art related to this technology is not provided in the abstract.
Frequently Updated Research
There is no information on frequently updated research relevant to this technology.
Questions about Point Cloud Encoding/Decoding
How does the dynamic list of prediction data improve the encoding process?
The dynamic list of prediction data allows for more accurate encoding by updating the predictions based on the residuals radius of decoded points during the process.
What are the potential applications of this technology beyond point cloud data encoding?
The technology can be applied in various fields such as 3D modeling, virtual reality, autonomous driving, and robotics for efficient data representation and processing.
Original Abstract Submitted
A method of encoding/decoding a point cloud into/from a bitstream of encoded point cloud data representing a physical object includes determining a dynamic list of at least one prediction data used to derive at least one candidate predictor used to encode geometry data of points of the point cloud. The list of at least one prediction data is dynamic because during the encoding of points, prediction data are updated based on residuals radius of decoded points.
