Deep Neural Network Encoding for 3D Data Compression
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Solution Overview
Problem
Three-dimensional data requires large storage capacity and imposes significant communication loads due to its extensive data volume, necessitating effective compression methods for efficient storage and transmission.
Innovation Solution
An encoding method utilizing deep neural networks (DNNs) to compress three-dimensional data by transforming high-dimensional data into lower-dimensional representations, allowing for efficient encoding and decoding while reducing data volume and communication loads.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If three-dimensional data is stored or transmitted without compression, then the original data quality and completeness are maintained, but the storage capacity requirements and communication loads become extremely large
Solution Approach 1:
The patent extracts and encodes only the essential geometric features and attributes of three-dimensional data points using deep neural networks, transforming the complete high-dimensional data into a compressed representation that captures the most important structural information while discarding redundant details
Solution Approach 2:
The patent changes the parameter representation of three-dimensional data by transforming spatial coordinates and attributes through learned neural network transformations, converting the data into a compressed latent space representation that maintains essential geometric relationships while reducing data volume
2Quantity of substance
If conventional compression methods are used for three-dimensional data, then some data volume reduction is achieved, but the compression rates are insufficient for efficient storage and transmission
Solution Approach 1:
The patent replaces conventional mechanical compression algorithms with a deep neural network-based encoding system that learns optimal compression transformations from data, enabling significantly higher compression rates by capturing complex geometric patterns that traditional algorithms cannot exploit
Solution Approach 2:
The patent transforms the compression approach by changing from fixed algorithmic parameter reduction to adaptive parameter transformation through learned neural network mappings, allowing the system to optimize compression for different three-dimensional data structures and achieve superior compression ratios
3Quantity of substance
If deep neural network encoding is applied to three-dimensional data, then high compression rates are achieved, but the encoding and decoding process becomes more complex
Solution Approach 1:
The patent introduces a trained deep neural network model as an intermediary that performs the complex encoding and decoding transformations, shifting the computational complexity from the runtime encoding/decoding process to the offline model training phase, thereby simplifying the operational complexity during actual data processing
Solution Approach 2:
The patent performs the complex neural network encoding transformations in advance during the model training phase, creating a pre-trained encoder that can be efficiently applied to compress three-dimensional data without requiring complex runtime computation, thereby reducing operational complexity
Data Source
AI summary
An encoding method according to the present disclosure includes: inputting three-dimensional data including three-dimensional coordinate data to a deep neural network (DNN); encoding the three-dimensional data by the DNN to generate encoded three-dimensional data; and outputting the encoded three-dimensional data.