Point Cloud Frame Partitioning for Spatial Random Access
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Solution Overview
Problem
Current technologies face challenges in efficiently compressing point clouds for real-time communications and dynamic mapping applications, particularly in reducing data volume while maintaining quality, and in enabling lossless compression for autonomous driving and cultural heritage applications.
Innovation Solution
The proposed solution involves a video-based point cloud compression (V-PCC) scheme that utilizes generic video codecs to compress the geometry, occupancy, and texture of dynamic point clouds as separate video sequences, with additional metadata compression, allowing for partial encoding, delivery, and decoding of coded point cloud bitstreams, including frame partition information and 3D bounding box data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If point cloud data is compressed using traditional methods, then data volume is reduced, but compression efficiency and quality maintenance are insufficient
Solution Approach 1:
The point cloud data is divided into multiple sub-frames, each representing a specific spatial region. This segmentation allows independent processing and compression of different regions, improving overall compression efficiency while maintaining quality through region-specific optimization
Solution Approach 2:
The patent transforms 3D point cloud data into 2D picture representations through projection and mapping operations. This dimensional transformation enables the use of efficient 2D video compression algorithms while preserving the essential 3D spatial information through metadata
2Reliability
If complete point cloud data is transmitted, then quality is maintained, but data transmission volume is excessive
Solution Approach 1:
The patent extracts and transmits only essential components: 2D picture data representing visual information and compact metadata containing 3D spatial information. This selective extraction maintains reconstruction quality while dramatically reducing transmission volume compared to sending complete point cloud data
Solution Approach 2:
The patent transforms point cloud data from its native 3D coordinate representation into 2D picture parameters and metadata parameters. This parameter transformation enables efficient compression while preserving the ability to reconstruct high-quality point cloud data at the decoder
3Ease of operation
If frame partitioning is implemented, then spatial random access and parallel processing are enabled, but encoding complexity increases
Solution Approach 1:
The frame is partitioned into multiple sub-frames corresponding to different spatial regions. Each sub-frame can be independently encoded and decoded, enabling spatial random access and parallel processing. The segmentation is organized systematically to balance the trade-off between accessibility and encoding complexity
Data Source
AI summary
Systems and methods for encoding a video stream are provided. A method includes signaling partitioning information in a coded bitstream that is based on a point cloud. The coded bitstream may be a coded video stream that includes a frame of a plurality of two-dimensional (2D) pictures that are layers of the frame, each of the plurality of 2D pictures having a respective attribute of a same three-dimensional (3D) representation; frame partition information that indicates the frame is partitioned into a plurality of sub-frames, each of the plurality of sub-frames being a respective combination of a sub-region of each picture of the plurality of 2D pictures; and 3D bounding box information that specifies a 3D position corresponding to a sub-frame of the plurality of sub-frames.