Semantic Communication for Efficient Point Cloud Transmission

2024-09-05

Global Communications Conference

AI Summary

To address the challenge of efficiently and reliably transmitting massive point cloud data—acquired by LiDAR and other 3D sensors—over bandwidth-constrained and noisy channels, this paper proposes an end-to-end semantic communication–driven point cloud transmission framework. Departing from conventional geometry-based sampling and compression paradigms, the framework introduces a novel parallel global–local semantic encoding architecture, implemented as a five-module deep neural network comprising local and global semantic encoders, a channel encoder–decoder, and a semantic decoder, jointly optimized via a semantic fidelity loss. Experimental results demonstrate that, under severe channel noise, the framework achieves a reconstructed PSNR of 37.2 dB—significantly outperforming octree-based methods and state-of-the-art deep learning approaches—thereby substantially enhancing reconstruction quality and robustness for point clouds in adverse channel conditions.

Technology Category

• Search and Optimization: Learning to Search
• Computer Vision: Learning & Optimization for CV
• Natural Language Processing: Sentence-level Semantics, Textual Inference, etc.

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Large language models for search
• Systems and Infrastructure for Web, Mobile and WoT: Data management and stream processing for Web, mobile and wireless applications

Abstract

As three-dimensional acquisition technologies like LiDAR cameras advance, the need for efficient transmission of 3D point clouds is becoming increasingly important. In this paper, we present a novel semantic communication (SemCom) approach for efficient 3D point cloud transmission. Different from existing methods that rely on downsampling and feature extraction for compression, our approach utilizes a parallel structure to separately extract both global and local information from point clouds. This system is composed of five key components: local semantic encoder, global semantic encoder, channel encoder, channel decoder, and semantic decoder. Our numerical results indicate that this approach surpasses both the traditional Octree compression methodology and alternative deep learning-based strategies in terms of reconstruction quality. Moreover, our system is capable of achieving high-quality point cloud reconstruction under adverse channel conditions, specifically maintaining a reconstruction quality of over 37dB even with severe channel noise.

Problem

• Efficient transmission of 3D point clouds
• Overcoming limitations of traditional compression methods
• High-quality reconstruction under adverse channel conditions

Innovation

• Parallel structure extracts global and local information
• Five-component system for semantic encoding and decoding
• High-quality reconstruction under adverse channel conditions