Shared-Parameter Point Cloud Compression for Lower Compute Load
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Solution Overview
Problem
Existing point cloud compression methods are inefficient and computationally expensive, particularly for consumer devices with limited processing power, and lack a unified framework for integrating different point cloud representation formats.
Innovation Solution
A hybrid coding framework that utilizes tree-based, voxel-based, and point-based coding strategies for different bit levels of point clouds, employing neural networks with shared parameters for encoding and decoding to optimize compression and processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional point cloud compression methods are used, then compression is achieved, but computational cost is high and processing efficiency is low
Solution Approach 1:
The point cloud data is divided into multiple sub-blocks for parallel processing. Each sub-block is processed independently by separate neural network sub-networks, enabling distributed computation and improving processing efficiency while reducing the computational burden on a single processing unit.
Solution Approach 2:
The neural network architecture dynamically adjusts the number of sub-networks based on the complexity and size of the point cloud data. This dynamic configuration optimizes computational resources by matching the processing capacity to the actual data requirements, reducing unnecessary computational cost.
2Adaptability or versatility
If different point cloud representation formats are integrated, then a unified framework is achieved, but system complexity increases
Solution Approach 1:
A universal neural network framework is designed that can handle multiple point cloud representation formats (point-based, voxel-based, tree-based) through a unified architecture. The same basic neural network structure processes different data types by adapting to their specific characteristics, eliminating the need for separate processing pipelines for each format.
Solution Approach 2:
Different components of the neural network are specialized for specific representation formats while maintaining overall framework unity. Each sub-network can be configured with appropriate parameters and structures suited to particular data types, allowing the system to process diverse formats efficiently without requiring complete system redesign.
Data Source
AI summary
In one implementation, a method of decoding point cloud data for a point cloud is presented. A signal indicative of a number of sub-blocks, N1 is decoded, and a neural network is configured to have N1 sub-blocks. In particular, each sub-block of the N1 sub-blocks includes an upsampling function and at least a neural network layer, and each of the N1 sub-blocks is configured with the same neural network parameters. The point cloud data is decoded based on the neural network. At the encoder side, the signal indicative of N1 is encoded, and the neural network is configured to have N1 sub-blocks. In particular, each sub-block of the N1 sub-blocks includes a downsampling function and at least a neural network layer, and each of the N1 sub-blocks is configured with the same neural network parameters. The point cloud data is encoded based on the neural network.