This paper presents novel solutions for the efficient and reliable transmission of 3D point clouds over wireless channels. We first propose SEPT for the transmission of small-scale point clouds, which encodes the point cloud via an iterative downsampling and feature extraction process. At the receiver, SEPT decoder reconstructs the point cloud with latent reconstruction and offset-based upsampling. A novel channel-adaptive module is proposed to allow SEPT to operate effectively over a wide range of channel conditions. Next, we propose OTA-NeRF, a scheme inspired by neural radiance fields. OTA-NeRF performs voxelization to the point cloud input and learns to encode the voxelized point cloud into a neural network. Instead of transmitting the extracted feature vectors as in the SEPT scheme, it transmits the learned neural network weights over the air in an analog fashion along with few hyperparameters that are transmitted digitally. At the receiver, the OTA-NeRF decoder reconstructs the original point cloud using the received noisy neural network weights. To further increase the bandwidth efficiency of the OTA-NeRF scheme, a fine-tuning algorithm is developed, where only a fraction of the neural network weights are retrained and transmitted. Extensive numerical experiments confirm that both the SEPT and the OTA-NeRF schemes achieve superior or comparable performance over the conventional approaches, where an octree-based or a learning-based point cloud compression scheme is concatenated with a channel code. As an additional advantage, both schemes mitigate the cliff and leveling effects making them particularly attractive for highly mobile scenarios, where accurate channel estimation is challenging if not impossible.

翻译：本文提出了在无线信道上高效可靠传输三维点云的新颖解决方案。我们首先提出了用于小规模点云传输的SEPT方案，该方案通过迭代下采样和特征提取过程对点云进行编码。在接收端，SEPT解码器通过潜在重建和基于偏移量的上采样来重建点云。我们提出了一种新颖的信道自适应模块，使SEPT能够在各种信道条件下有效工作。其次，我们提出了受神经辐射场启发的OTA-NeRF方案。OTA-NeRF对输入点云进行体素化处理，并学习将体素化点云编码到神经网络中。与SEPT方案中传输提取的特征向量不同，该方案以模拟方式在空中传输学习得到的神经网络权重，并辅以少量通过数字方式传输的超参数。在接收端，OTA-NeRF解码器利用接收到的含噪神经网络权重重建原始点云。为进一步提升OTA-NeRF方案的带宽效率，我们开发了微调算法，仅对部分神经网络权重进行重新训练和传输。大量数值实验证实，相较于传统方法（将基于八叉树或基于学习的点云压缩方案与信道编码级联），SEPT和OTA-NeRF方案均实现了更优或相当的性能。作为额外优势，这两种方案有效缓解了悬崖效应和平稳效应，使其在高速移动场景中尤为适用——这类场景中精确的信道估计即使可能也极具挑战性。