  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.
