Point Cloud Transmission with Octree Encoding and Attribute Prediction
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Solution Overview
Problem
The transmission and reception of point cloud data is challenging due to its large volume, requiring high throughput and complex, time-consuming encoding and decoding processes, which are exacerbated by the large number of points in three-dimensional space.
Innovation Solution
A method and apparatus for encoding and decoding point cloud data using an octree structure to quantize geometry information and predict attribute information based on structural adjacency, reducing computational complexity and latency through entropy encoding.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If point cloud data is transmitted with high quality, then the user experience for VR, AR, MR and autonomous driving services is improved, but the data volume increases requiring high throughput transmission
Solution Approach 1:
The point cloud data is segmented into multiple layers including a first layer with first-point clouds and a second layer with second-point clouds. This segmentation allows differential encoding where different layers can be encoded with different complexities, reducing overall data volume while maintaining quality for critical regions.
Solution Approach 2:
Different encoding precision is applied to different regions of the point cloud. The first layer uses a first encoding precision while the second layer uses a second encoding precision. This local quality approach ensures high quality where needed while reducing data volume in less critical areas.
2Loss of substance
If encoding is performed on large volume point cloud data, then compression is achieved, but the encoding and decoding process becomes complicated and time-consuming
Solution Approach 1:
The encoding process is segmented into multiple stages: generating a first encoded bitstream from the first layer, then generating a second encoded bitstream from the second layer using the first encoded bitstream as reference. This segmentation reduces the complexity of encoding the entire point cloud at once.
Solution Approach 2:
The first layer is encoded before the second layer, and the first encoded bitstream is used as a reference for encoding the second layer. This preliminary action simplifies the overall encoding process by establishing a foundation that guides subsequent encoding steps.
3Loss of substance
If encoding is performed on large volume point cloud data, then compression is achieved, but the decoding time increases becoming time-consuming
Solution Approach 1:
The decoded point cloud data is segmented into multiple layers that can be decoded in sequence. The first decoded point cloud data is generated from the first encoded bitstream, and the second decoded point cloud data is generated from the second encoded bitstream using the first decoded data as reference. This segmentation enables faster incremental decoding.
4Productivity
If multiple layers of point cloud data are encoded with different precisions, then compression performance is improved, but the encoding process becomes more complex
Solution Approach 1:
The encoding apparatus is designed to perform multiple functions: it can encode point cloud data at different precisions for different layers, use previously encoded data as reference for subsequent encoding, and generate multiple encoded bitstreams. This multi-functionality is achieved through a unified encoding structure that handles variable precision requirements.
Data Source
AI summary
A point cloud data transmission method according to embodiments can comprise the steps of: acquiring point cloud data; encoding geometry information included in the point cloud data; encoding attribute information included in the point cloud data; and transmitting the encoded geometry information and the encoded attribute information.