Autoencoder Point Cloud Compression with Cross-Attention
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Solution Overview
Problem
Existing point cloud compression technologies do not effectively explore the correlation between point cloud data, resulting in low compression ratios and inefficient storage and transmission of large datasets.
Innovation Solution
The method involves dividing point cloud data into intra-frame and inter-frame point clouds, generating a motion vector, and using an autoencoder network with down-sampling convolution layers and cross-attention layers to compress the inter-frame point cloud, capturing correlations between frames.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If existing point cloud compression technologies are used, then the compression process is simple, but the compression ratio is low and data correlation is not effectively explored
Solution Approach 1:
The patent segments point cloud data into multiple frames and further divides each frame into intra-frame and inter-frame components. This segmentation enables targeted compression strategies for different frame types, improving overall compression ratio while managing complexity through structured organization of the compression system
Solution Approach 2:
The patent performs preliminary actions by generating motion vectors before the main compression process and pre-processing point cloud frames to identify temporal correlations. This preliminary analysis enables more efficient compression by preparing correlation data in advance, improving compression ratio without proportionally increasing system complexity
2Quantity of substance
If motion vectors and autoencoder networks are used to capture frame correlations, then compression ratio improves, but computational complexity increases
Solution Approach 1:
The patent applies partial action by using autoencoder networks selectively for inter-frame compression rather than processing all frames uniformly. The network processes only the residual differences between frames, reducing the overall computational power required while achieving better compression ratios for the most compressible data portions
Solution Approach 2:
The patent changes parameters by adjusting the complexity of the autoencoder network architecture and motion vector precision based on scene characteristics. This adaptive parameter adjustment allows the system to achieve high compression ratios when needed while reducing computational power consumption for simpler scenes, effectively managing the trade-off between compression and computation
3Productivity
If cross-attention layers are used to capture correlation between frames, then compression efficiency improves, but model complexity increases
Solution Approach 1:
The patent implements multi-functionality by designing the cross-attention mechanism to simultaneously capture temporal correlations between frames and spatial correlations within frames. This universal approach improves compression efficiency by handling multiple types of correlations with a single mechanism rather than requiring separate complex modules for each correlation type
Solution Approach 2:
The patent introduces the cross-attention mechanism as an intermediary component that mediates between the input point cloud frames and the compression output. This intermediary efficiently captures complex inter-frame correlations that would otherwise require much more complex direct processing, improving compression efficiency while managing model complexity through the attention mechanism's inherent efficiency
Data Source
AI summary
A method includes generating an intra-frame point cloud and an inter-frame point cloud based on point cloud data that includes a plurality of point cloud frames. The method further includes generating a motion vector associated with the intra-frame point cloud based on the intra-frame point cloud and the inter-frame point cloud. The method further includes generating a compressed inter-frame point cloud based on the inter-frame point cloud by using an encoder module of an autoencoder network. In addition, the method further includes generating compressed point cloud data based on the motion vector and the compressed inter-frame point cloud. According to solutions of embodiments of the present disclosure, the point cloud data can be compressed through a down-sampling convolution layer of the encoder module, and a correlation between the point cloud data can be captured by combining cross-attention layers, resulting in improved compression ratio, compression accuracy and point cloud data quality.