Point Cloud Positional Encoding for Geometry Compression
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Solution Overview
Problem
Existing point cloud compression technologies face challenges in efficiently encoding and decoding large-scale point clouds with both geometry and attributes, particularly in dynamic scenarios, leading to suboptimal compression efficiency.
Innovation Solution
Incorporation of positional encoding layers into encoder and decoder networks using deep learning techniques to generate higher-dimensional features, which are combined with existing feature tensors to enhance the efficiency of point cloud compression.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional point cloud compression methods are used, then the compression process is simpler, but the compression efficiency is suboptimal
Solution Approach 1:
The patent applies dimensional transformation by converting 3D point cloud coordinates into 2D image representations through projection. This dimensionality reduction enables the use of efficient 2D compression algorithms while preserving essential spatial relationships, thereby improving compression efficiency without excessively increasing system complexity
Solution Approach 2:
The encoder network is segmented into multiple specialized modules: a projection module that converts 3D points to 2D coordinates, a feature extraction module that processes geometric and attribute features separately, and a compression module that handles the encoded data. This segmentation allows each component to be optimized independently, improving overall compression efficiency while managing complexity through modular design
2Quantity of substance
If downsampling is applied to reduce point cloud size, then data volume decreases, but geometric precision is lost
Solution Approach 1:
The patent performs preliminary feature extraction and encoding on the full-resolution point cloud before any downsampling operations. By extracting geometric and attribute features from the complete dataset first, then applying downsampling only to the encoded representations, the method preserves maximum geometric precision while still achieving data volume reduction for efficient compression
Solution Approach 2:
The feature extraction process applies different processing quality levels to different aspects of the point cloud data. Geometric features (positions, normals, curvatures) are extracted with high precision to maintain shape accuracy, while attribute features (colors, materials) are processed separately. This local quality differentiation allows downsampling without significant loss of geometric precision
3Productivity
If deep learning networks are used for encoding, then compression efficiency improves, but computational complexity increases
Solution Approach 1:
The patent replaces traditional mechanical compression algorithms with deep learning-based neural networks for feature extraction and encoding. The encoder uses convolutional neural networks (CNNs) to automatically learn optimal compression representations, achieving superior compression efficiency. The decoder uses corresponding neural network models to reconstruct the point cloud, substituting complex computational processes with trained models that require less real-time computation
Data Source
AI summary
A point cloud encoder is configured to receive a frame of point cloud data and encode coordinates of a geometry of the frame of point cloud data using a deep learning network. The deep learning network includes one or more layers configured to generate first features for the coordinates of the geometry, and at least one positional encoding layer configured to generate second features for the coordinates of the geometry and combine the first features with the second features to generate higher-dimensional features. The point cloud encoder may output an output tensor comprising encoded coordinates and the higher-dimensional features.