Semantic-aware Transmission for Robust Point Cloud Classification

TL;DR

This paper presents a semantic-aware transmission system for point cloud classification that leverages pre-trained models to improve robustness and accuracy under noisy channel conditions, demonstrating significant performance gains.

Contribution

It introduces a novel semantic-aware communication framework with a two-stage training strategy, enhancing robustness and efficiency in point cloud classification over noisy channels.

Findings

Achieves over 89% accuracy at SNR > 10 dB

Maintains above 66.6% accuracy at SNR of 4 dB

Outperforms existing methods by up to 48% in accuracy improvements

Abstract

As three-dimensional (3D) data acquisition devices become increasingly prevalent, the demand for 3D point cloud transmission is growing. In this study, we introduce a semantic-aware communication system for robust point cloud classification that capitalizes on the advantages of pre-trained Point-BERT models. Our proposed method comprises four main components: the semantic encoder, channel encoder, channel decoder, and semantic decoder. By employing a two-stage training strategy, our system facilitates efficient and adaptable learning tailored to the specific classification tasks. The results show that the proposed system achieves classification accuracy of over 89\% when SNR is higher than 10 dB and still maintains accuracy above 66.6\% even at SNR of 4 dB. Compared to the existing method, our approach performs at 0.8\% to 48\% better across different SNR values, demonstrating robustness to channel noise. Our system also achieves a balance between accuracy and speed, being computationally efficient while maintaining high classification performance under noisy channel conditions. This adaptable and resilient approach holds considerable promise for a wide array of 3D scene understanding applications, effectively addressing the challenges posed by channel noise.