Point Cloud Data Transmission via 3D to 2D Projection
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Solution Overview
Problem
Existing methods face challenges in efficiently transmitting and receiving point cloud data due to its large size and complexity, leading to latency and encoding/decoding issues, which hinder the delivery of high-quality point cloud services for applications like virtual reality, augmented reality, and autonomous driving.
Innovation Solution
A method involving encoding point cloud data into a bitstream and using a point cloud data transmission device that includes a video acquirer, encoder, file/segment encapsulator, and transmitter, along with a reception device that decapsulates and decodes the bitstream, enabling efficient transmission and rendering of point cloud content.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If point cloud data is transmitted with high quality and low latency, then service quality improves, but encoding/decoding complexity increases
Solution Approach 1:
The point cloud data is divided into multiple patches, which are then processed and transmitted independently. This segmentation allows the encoding and decoding processes to work on smaller, manageable units rather than the entire large-scale point cloud dataset, thereby reducing computational complexity while maintaining overall service quality.
Solution Approach 2:
The patent transforms the three-dimensional point cloud data into two-dimensional image data through projection and mapping operations. This dimensionality change enables the use of efficient 2D image coding techniques for compression and transmission, significantly reducing encoding/decoding complexity while preserving the essential spatial information needed for high-quality service delivery.
2Productivity
If point cloud data is compressed to reduce transmission bandwidth, then transmission efficiency improves, but decoding complexity increases
Solution Approach 1:
By converting 3D point cloud data to 2D image representation, the patent enables application of mature 2D image compression standards and techniques. This approach achieves high transmission efficiency through effective compression while the decoding process benefits from well-optimized 2D image decoding algorithms, reducing overall decoding complexity compared to direct 3D point cloud decoding.
Solution Approach 2:
The patent creates a 2D image copy or representation of the 3D point cloud data. This copied 2D representation can be compressed and transmitted efficiently using standard image compression techniques, while the receiving end decodes this simpler 2D data structure rather than dealing with the full complexity of the original 3D point cloud, thereby reducing decoding complexity.
3Device complexity
If the number of points in the point cloud is reduced to simplify processing, then processing complexity decreases, but data precision is lost
Solution Approach 1:
The transformation of 3D point cloud to 2D image data preserves the essential spatial and visual information in a different representation format. This dimensional transformation maintains data precision for visual reconstruction purposes while significantly simplifying subsequent processing operations, as 2D image processing is generally less complex than 3D point cloud processing.
Data Source
AI summary
Disclosed is a method of transmitting point cloud data including encoding point cloud data, and transmitting a bitstream containing the point cloud data. Disclosed is a method of receiving point cloud data may include receiving a bitstream containing point cloud data, and decoding the point cloud data.