# Semantic-aware Transmission for Robust Point Cloud Classification

arXiv: https://arxiv.org/abs/2306.13296 · HTML: https://mlsanity.com/paper/2306.13296/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Jun 2023.

## Built on by (most cited first, 4 of 4)

- [Joint Semantic-Channel Coding and Modulation for Token Communications](https://mlsanity.com/paper/2511.15699/) (2025)
- [Enhancing Semantic Communication with Deep Generative Models -- An ICASSP Special Session Overview](https://mlsanity.com/paper/2309.02478/) (2023)
- [Channel-adaptive Cross-modal Generative Semantic Communication for Point Cloud Transmission](https://mlsanity.com/paper/2506.03211/) (2025)
- [Task-Oriented Wireless Transmission of 3D Point Clouds: Geometric Versus Semantic Robustness](https://mlsanity.com/paper/2603.13560/) (2026)

## Abstract (arXiv)

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.