Apple inc. (20240205430). Block-Based Predictive Coding For Point Cloud Compression simplified abstract
Contents
- 1 Block-Based Predictive Coding For Point Cloud Compression
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 Block-Based Predictive Coding For Point Cloud Compression - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Commercial Applications
- 1.9 Prior Art
- 1.10 Frequently Updated Research
- 1.11 Questions about Point Cloud Compression
- 1.12 Original Abstract Submitted
Block-Based Predictive Coding For Point Cloud Compression
Organization Name
Inventor(s)
Khaled Mammou of Danville CA (US)
Alexandros Tourapis of Los Gatos CA (US)
Jungsun Kim of San Jose CA (US)
Block-Based Predictive Coding For Point Cloud Compression - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240205430 titled 'Block-Based Predictive Coding For Point Cloud Compression
Simplified Explanation
An encoder compresses point cloud information using blocks of nodes determined from a prediction tree. The prediction tree is generated for a point cloud, and segments of the tree are identified. These segments are divided into blocks that are predicted by predecessor blocks within the segments. The encoded blocks of the prediction tree can be transmitted to a decoder to regenerate the original point cloud.
- The encoder compresses point cloud data using blocks of nodes from a prediction tree.
- A prediction tree is created for the point cloud data.
- Segments of the prediction tree are identified and divided into blocks.
- These blocks are predicted by predecessor blocks within the segments.
- The encoded blocks can be sent to a decoder for reconstruction of the point cloud.
Potential Applications
This technology can be applied in various fields such as 3D modeling, virtual reality, autonomous driving, and robotics where efficient compression and transmission of point cloud data are essential.
Problems Solved
This technology addresses the challenge of efficiently compressing and transmitting large amounts of point cloud data while ensuring accurate reconstruction at the receiving end.
Benefits
The benefits of this technology include improved data compression, faster transmission speeds, reduced bandwidth requirements, and accurate reconstruction of point cloud data.
Commercial Applications
- 3D modeling software
- Virtual reality applications
- Autonomous driving systems
- Robotics and automation industries
Prior Art
Readers can explore prior art related to this technology by researching advancements in point cloud compression, prediction algorithms, and data encoding techniques.
Frequently Updated Research
Researchers are continually exploring new methods to enhance point cloud compression, prediction tree generation, and data encoding algorithms to further improve efficiency and accuracy in reconstructing point cloud data.
Questions about Point Cloud Compression
How does point cloud compression impact data storage requirements?
Point cloud compression reduces the storage requirements by efficiently encoding and transmitting data, saving disk space and improving data management.
What are the key challenges in predicting blocks within segments of a prediction tree?
The main challenges include accurately predicting the content of blocks based on predecessor blocks and ensuring minimal loss of data during compression and reconstruction processes.
Original Abstract Submitted
an encoder is configured to compress point cloud information using a blocks of nodes determined from a prediction tree. a prediction tree is generated for a point cloud. segments of the prediction tree are identified. the segments are divided into blocks that are predicted by predecessor blocks within the segments. the blocks of the prediction tree may then be encoded and may be provided for transmission to a decoder that can regenerate the point cloud from the blocks of the prediction tree.
