18060208. LOCAL ADAPTIVE INTER PREDICTION FOR G-PCC simplified abstract (QUALCOMM Incorporated)
Contents
LOCAL ADAPTIVE INTER PREDICTION FOR G-PCC
Organization Name
Inventor(s)
Luong Pham Van of San Diego CA (US)
Geert Van Der Auwera of San Diego CA (US)
Adarsh Krishnan Ramasubramonian of Irvine CA (US)
Marta Karczewicz of San Diego CA (US)
LOCAL ADAPTIVE INTER PREDICTION FOR G-PCC - A simplified explanation of the abstract
This abstract first appeared for US patent application 18060208 titled 'LOCAL ADAPTIVE INTER PREDICTION FOR G-PCC
Simplified Explanation
The patent application describes a method for decoding point cloud data, which involves splitting the data into multiple largest prediction units (LPUs) that have different sizes along different directions. The method then uses inter prediction to determine predicted points for the LPUs and reconstructs the points within the LPUs based on these predictions.
- The method splits point cloud data into largest prediction units (LPUs).
- LPUs can have different sizes along different directions.
- Inter prediction is performed to determine predicted points for the LPUs.
- Points within the LPUs are reconstructed based on the predicted points.
Potential Applications
- Point cloud data compression and transmission.
- 3D modeling and visualization.
- Autonomous driving and robotics.
Problems Solved
- Efficient decoding of point cloud data.
- Handling point cloud data with varying sizes along different directions.
- Accurate reconstruction of points within LPUs.
Benefits
- Improved compression and transmission efficiency.
- Enhanced 3D modeling and visualization capabilities.
- More accurate and reliable autonomous driving and robotics systems.
Original Abstract Submitted
A method of decoding point cloud data comprises: determining that the point cloud data is split into a plurality of largest prediction units (LPUs), wherein at least two of the LPUs have different sizes along different directions; performing inter prediction to determine predicted points for the LPUs; and reconstructing points within the LPUs based on the predicted points for the LPUs.
