POST-MIMO: Partition-Oriented Semantic Transmission for MIMO Point Cloud Delivery
Abstract
Point clouds have become a fundamental data format for emerging three dimensional (3D) applications, while their wireless delivery remains challenging due to the large data volume and irregular geometric structure. Existing semantic point cloud transmission methods are mainly developed for single-input single-output systems, making them difficult to apply directly to multiple-input multiple-output (MIMO) systems. To address this issue, we propose a partition-oriented semantic transmission framework for MIMO (POST-MIMO) point cloud delivery. POST-MIMO partitions the input point cloud into spatially coherent blocks and aligns them with parallel MIMO streams. Based on this block-aligned representation, the transmitter employs a hierarchical encoder to capture both intra-block local geometry and inter-block global dependency, while the auxiliary side information is transmitted jointly to assist reconstruction. At the receiver, a progressive decoder reconstructs the block geometry in a progressive manner and restores the final point cloud with block-aware adjustment. Experiments demonstrate improved reconstruction quality and lower computational resource consumption than representative reference schemes under the considered MIMO setting.
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BibTeX
@misc{yue2026postmimo,
title = {{POST-MIMO: Partition-Oriented Semantic Transmission for MIMO Point Cloud Delivery}},
author = {Hang Yue and Junlin Chang and Tianqing Zhao and Yulin Zhao and Rongke Liu},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.41253},
eprint = {2610.41253},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.41253},
}
RIS
TY - UNPB
TI - POST-MIMO: Partition-Oriented Semantic Transmission for MIMO Point Cloud Delivery
AU - Hang Yue
AU - Junlin Chang
AU - Tianqing Zhao
AU - Yulin Zhao
AU - Rongke Liu
PY - 2026
DA - 2026/10/30
PB - arXiv
DO - 10.48550/arxiv.2610.41253
UR - https://arxiv.org/abs/2610.41253
AB - Point clouds have become a fundamental data format for emerging three dimensional (3D) applications, while their wireless delivery remains challenging due to the large data volume and irregular geometric structure. Existing semantic point cloud transmission methods are mainly developed for single-input single-output systems, making them difficult to apply directly to multiple-input multiple-output (MIMO) systems. To address this issue, we propose a partition-oriented semantic transmission framework for MIMO (POST-MIMO) point cloud delivery. POST-MIMO partitions the input point cloud into spatially coherent blocks and aligns them with parallel MIMO streams. Based on this block-aligned representation, the transmitter employs a hierarchical encoder to capture both intra-block local geometry and inter-block global dependency, while the auxiliary side information is transmitted jointly to assist reconstruction. At the receiver, a progressive decoder reconstructs the block geometry in a progressive manner and restores the final point cloud with block-aware adjustment. Experiments demonstrate improved reconstruction quality and lower computational resource consumption than representative reference schemes under the considered MIMO setting.
ER -