Distribution-Aware Point Feature Extraction for 3D Compression
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Solution Overview
Problem
Existing point cloud compression techniques struggle with efficient encoding and decoding of 3D point cloud data, particularly in applications like autonomous driving and AR/VR, due to the high complexity and computational demands of processing large volumes of spatial data.
Innovation Solution
A learning-based point cloud geometry processing block method that includes normalizing and transforming feature maps using distribution parameters, encoding them into bitstreams, and utilizing neural networks like sparse CNN and ResNet for efficient encoding and decoding.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If traditional point cloud compression techniques are used, then the method is simpler to implement, but the computational complexity and processing time increase significantly
Solution Approach 1:
The patent replaces traditional mechanical compression algorithms with a deep learning-based system that uses neural networks to automatically learn optimal compression parameters and transformations. The neural network model substitutes complex manual compression operations with learned patterns, achieving faster processing while maintaining compression efficiency.
Solution Approach 2:
The patent changes the compression parameters dynamically based on the input point cloud characteristics. The system adjusts transformation parameters, quantization levels, and encoding settings according to the specific geometry and distribution of the input data, enabling adaptive compression that optimizes processing speed for each scenario.
2Measurement precision
If more neural network layers are added to improve feature extraction, then the encoding accuracy improves, but the computational complexity increases
Solution Approach 1:
The patent segments the neural network into modular components, including separate feature extraction modules, transformation modules, and encoding modules. Each module processes specific aspects of the point cloud data independently, allowing the system to achieve high encoding accuracy through coordinated action of multiple segments without requiring a single monolithic complex network.
Solution Approach 2:
The patent applies partial action by using a limited number of neural network layers that are sufficient for the compression task at hand. Rather than employing excessively deep networks that would over-compute, the system uses an optimized depth that provides adequate feature extraction without unnecessary computational overhead, achieving the right balance between accuracy and complexity.
3Productivity
If distribution parameters are transformed to normalize feature maps, then the encoding efficiency improves, but the processing time increases
Solution Approach 1:
The patent performs preliminary action by pre-computing and storing distribution parameters and transformation matrices during a training phase. During actual encoding, these pre-computed parameters are simply applied to the input feature maps through efficient matrix multiplication, avoiding the need to perform complex statistical analysis and normalization in real-time, thus improving encoding efficiency while minimizing processing time.
Data Source
AI summary
Some embodiments of a method may include a learning-based point cloud geometry processing block method, the method including: accessing a first feature map, wherein the first feature map has a quantity of C channels and is an input to the processing block, and wherein the first feature map is generated by a first set of neural network layers; accessing a set of distribution parameters; transforming the first feature map to a second feature map based on the set of distribution parameters; and encoding the second feature map into a bitstream. These example processes may be applicable to both the encoder and the decoder of an AI-based point cloud compression (PCC) framework.