Point Cloud Transmission With Patch-Based Rate Adaptation
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.