Point Cloud Reconstruction Modes for Lower Bit-Rate Compression
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Solution Overview
Problem
Existing technologies face challenges in efficiently compressing dynamic point clouds for distribution while maintaining high quality and reducing bit-rate consumption, particularly in applications like virtual reality and autonomous vehicles.
Innovation Solution
A method for signaling a Point Local Reconstruction mode in a bitstream, allowing for efficient encoding and decoding of point clouds through a two-layer-based structure, including a base layer for lossy representation and an enhancement layer for additional details, using existing video codecs to compress geometry and texture information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If dynamic point clouds are compressed for distribution, then bit-rate consumption is reduced, but quality of experience deteriorates
Solution Approach 1:
The patent segments the point cloud data into multiple layers (base layer and enhancement layers), where each layer provides progressively higher quality reconstruction. The base layer provides essential geometry at lower bit-rate, while enhancement layers add progressively more detail, allowing receivers to reconstruct point clouds at different quality levels based on available bandwidth.
Solution Approach 2:
The patent employs dynamic mode selection and adaptive parameter adjustment based on the characteristics of the point cloud data and transmission conditions. Different reconstruction modes (e.g., copy mode, interpolated mode, filtered mode) are dynamically selected for different regions or time frames to optimize the balance between quality and bit-rate.
2Reliability
If compression algorithms are made more complex to maintain quality, then quality of experience is improved, but device complexity increases
Solution Approach 1:
The patent divides the complex compression process into separate processing stages: base layer encoding, enhancement layer encoding, and multiple reconstruction modes. Each stage handles specific aspects of the compression, making the overall system more manageable and implementable while maintaining high quality through the layered approach.
Solution Approach 2:
The patent uses reference frames and motion compensation techniques where previously decoded point cloud frames are copied and reused as references for predicting current frames. This reduces the amount of new data that needs to be encoded and transmitted, lowering complexity while maintaining quality through temporal redundancy exploitation.
Data Source
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Figure 3
Figure 3a
AI summary
At least one embodiment relates to a method for signaling a syntax element representing a Point Local Reconstruction mode, said Point Local Reconstruction mode being representative of at least one parameter defining a mode for reconstructing at least one point of a point cloud frame.