Point Cloud Transmission With Patch-Based Rate Adaptation
Abstract
We propose a joint source-channel coding (JSCC) framework for efficient wireless point cloud transmission. In the proposed scheme, the point cloud is first divided into local patches and transformed into compact latent representations, which are then mapped to symbols for transmission over a noisy channel. Our model incorporates an entropy model that exploits inter-patch spatial dependencies to estimate patch-level coding costs and allocate different numbers of symbols accordingly. Unlike existing patch-based point cloud JSCC methods that use the same transmission rate between patches, we adapt the channel usage of each patch latent representation. To exploit inter-patch dependencies without patch-by-patch autoregressive processing, we select a group of anchor patches whose features provide contextual information for entropy estimation of non-anchor ones. The framework is trained using a reconstruction loss and an entropy-based rate surrogate. Experiments on ModelNet40 demonstrate overall rate-distortion improvements over existing baselines across channel conditions.
Related
- POST-MIMO: Partition-Oriented Semantic Transmission for MIMO Point Cloud Delivery
- Generalized Wasserstein Flow Matching: Nested Couplings for Point Cloud Generation
- MANBO: Mamba-Based Lightweight Attention Network with Priority-Bridged Ordering for Point Cloud Processing
- Gaussian Linear Functional Manifold Method for Massive Point Cloud Data
- Sustainable Cloud-Fog-Edge Systems for Smart Buildings: Algorithmic Adaptation under Memory Constraints and Dynamic Orchestration Strategies
BibTeX
@misc{liu2026point,
title = {{Point Cloud Transmission With Patch-Based Rate Adaptation}},
author = {Shipeng Liu and Garima Bazard and Khac-Hoang Ngo and Per-Erik Forssén},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.29479},
eprint = {2610.29479},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.29479},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Point Cloud Transmission With Patch-Based Rate Adaptation
AU - Shipeng Liu
AU - Garima Bazard
AU - Khac-Hoang Ngo
AU - Per-Erik Forssén
PY - 2026
DA - 2026/10/21
PB - arXiv
DO - 10.48550/arxiv.2610.29479
UR - https://arxiv.org/abs/2610.29479
AB - We propose a joint source-channel coding (JSCC) framework for efficient wireless point cloud transmission. In the proposed scheme, the point cloud is first divided into local patches and transformed into compact latent representations, which are then mapped to symbols for transmission over a noisy channel. Our model incorporates an entropy model that exploits inter-patch spatial dependencies to estimate patch-level coding costs and allocate different numbers of symbols accordingly. Unlike existing patch-based point cloud JSCC methods that use the same transmission rate between patches, we adapt the channel usage of each patch latent representation. To exploit inter-patch dependencies without patch-by-patch autoregressive processing, we select a group of anchor patches whose features provide contextual information for entropy estimation of non-anchor ones. The framework is trained using a reconstruction loss and an entropy-based rate surrogate. Experiments on ModelNet40 demonstrate overall rate-distortion improvements over existing baselines across channel conditions.
ER -