Point Cloud Bitstream Slicing for Low-Latency Decoding
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Solution Overview
Problem
Existing methods for transmitting point cloud data face challenges in efficiency, latency, and encoding/decoding complexity, particularly in real-time processing and scalability for applications like VR, AR, and self-driving services.
Innovation Solution
A method involving encoding point cloud data, partitioning it into slices, and transmitting a bitstream containing geometry and attribute data, with signaling information about partitioning, allowing for independent encoding and decoding on a slice-by-slice basis using methods like octree or axis-based partitioning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If point cloud data is transmitted as a whole without partitioning, then transmission simplicity is maintained, but encoding and decoding complexity increases and latency increases
Solution Approach 1:
The point cloud data is divided into multiple slices, where each slice contains a subset of points that can be independently encoded and decoded. This segmentation allows parallel processing of different slices, significantly improving encoding and decoding speed while reducing the computational complexity for each individual slice.
2Productivity
If point cloud data is partitioned into multiple slices, then encoding and decoding speed improves, but transmission overhead increases due to signaling information
Solution Approach 1:
Multiple slices are merged into a single bitstream for transmission, with signaling information embedded within the bitstream to indicate slice boundaries and properties. This merging approach allows the system to benefit from parallel encoding and decoding of slices while minimizing transmission overhead by consolidating data into a unified stream rather than transmitting separate streams for each slice.
3Loss of time
If point cloud data is processed in real-time with low latency requirements, then service quality improves, but encoding and decoding complexity increases
Solution Approach 1:
By dividing point cloud data into independent slices, the system enables parallel encoding and decoding operations across multiple slices. This parallelism significantly reduces the total processing time and latency, meeting real-time service requirements while keeping the complexity of each individual slice manageable through independent processing.
4Reliability
If point cloud data is divided into overlapping slices, then error propagation is prevented, but encoding complexity increases due to region of interest handling
Solution Approach 1:
The point cloud data is divided into multiple overlapping slices, where each slice covers a specific region of interest. The overlapping regions ensure that errors in one slice do not propagate to other slices, as each slice can be independently decoded. This segmentation strategy improves reliability by isolating errors while maintaining manageable encoding complexity through localized processing of each slice's region of interest.
Data Source
AI summary
Disclosed herein is a point cloud data reception device including a receiver configured to receive a bitstream containing geometry data of point cloud data and/or attribute data of the point cloud data, and/or a decoder configured to decode the point cloud data in the bitstream. Disclosed herein is a point cloud data transmission device including an encoder configured to encode point cloud data, and/or a transmitter configured to transmit a bitstream containing the point cloud data and/or signaling information about the point cloud data.