Point Cloud Geometric Compression via Depth Auto-Encoder
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Solution Overview
Problem
Existing point cloud geometric compression methods are inefficient in terms of compression performance, require lengthy design processes, and struggle to adapt quickly to new data sets, especially when using traditional manual feature-based methods.
Innovation Solution
A point cloud geometric compression method based on a depth auto-encoder framework, which includes preprocessing, down-sampling, training a neural network-based compression model, and applying it for geometric compression, utilizing a neural network encoder, quantizer, and decoder to optimize rate-distortion loss.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional octree-based geometric compression methods are used, then processing efficiency is high, but compression performance needs to be improved
Solution Approach 1:
The patent replaces the traditional mechanical octree-based compression system with a neural network-based deep learning system. The encoder and decoder networks learn optimal compression representations from data, substituting hand-crafted geometric algorithms with data-driven neural models that achieve superior compression performance while maintaining efficient processing.
Solution Approach 2:
The patent transforms the compression approach by changing from fixed octree parameters to learned neural network parameters. The encoder network learns optimal feature representations and the decoder learns optimal reconstruction parameters, allowing adaptive parameter optimization that improves compression performance compared to fixed octree methods.
2Reliability
If traditional manual feature-based methods are used, then the design process is established, but the development time is long and it does not adapt to new data sets quickly
Solution Approach 1:
The neural network-based system performs self-learning and self-optimization through automatic training on point cloud data. The encoder and decoder networks automatically adjust their parameters during training without manual feature engineering, enabling rapid adaptation to new data sets while maintaining reliable compression performance through learned representations.
Solution Approach 2:
The patent performs preliminary training of the neural network models on large point cloud datasets before actual compression tasks. This preliminary learning phase enables the system to quickly adapt to new data types and characteristics, reducing development time for new applications while maintaining design stability through transfer learning.
3Ease of manufacture
If traditional encoders are designed manually, then the framework is established, but a large amount of labor cost is required and compression efficiency needs improvement
Solution Approach 1:
The patent replaces manual encoder design with automated neural network training. The system uses deep learning frameworks to automatically learn optimal encoding strategies from data, eliminating the need for manual feature engineering and design iterations, thereby reducing labor costs while significantly improving compression efficiency through data-driven optimizations.
4Loss of information
If point cloud data is not down-sampled, then all geometric information is preserved, but the data volume is very large and processing becomes inefficient
Solution Approach 1:
The patent extracts essential geometric features from dense point clouds through neural network processing. The encoder network identifies and extracts key geometric characteristics, discarding redundant information, thereby reducing data volume for efficient processing while preserving the most important geometric information needed for accurate reconstruction.
Solution Approach 2:
The neural network applies different processing quality to different regions of the point cloud, focusing computational resources on geometrically significant areas while using coarser representation for less important regions. This local quality adaptation maintains processing efficiency while preserving critical geometric information.
Data Source
AI summary
The invention discloses a point cloud geometric compression method based on a depth auto-encoder, which comprises the following steps of: (1) point cloud preprocessing: collecting a type of point cloud to be compressed as a training set, and normalizing the training set into a unit circle; (2) point cloud down-sampling: down-sampling the point clouds in the training set so that each point cloud have the same point number m and each point in the point cloud has (x, y, z) three-dimensional coordinates; and adopting a farthest point down-sampling method by randomly selecting a point for the first time and then selecting the point farthest away from the selected point set every time to add into the selected point set until the selected point number meets the requirement; (3) training a compression model: inputting the point cloud sampled in step (2) into a point cloud geometric compression framework based on a depth auto-encoder for training; and (4) geometric compression of the point cloud: applying the trained compression framework to the geometric compression of all the point clouds of the type. The method can not only obtain a good compression effect but also adapt to a new point cloud type more quickly.