Abstract—In a fully autonomous driving framework, where vehicles
operate without human intervention, information sharing
plays a fundamental role. In this context, new network solutions
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have to be designed to handle the large volumes of data generated
by the rich sensor suite of the cars in a reliable and efficient
way. Among all the possible sensors, Light Detection and Ranging
(LiDAR) can produce an accurate 3D point cloud representation
of the surrounding environment, which in turn generates high
data rates. For this reason, efficient point cloud compression
is paramount to alleviate the burden of data transmission over
bandwidth-constrained channels and to facilitate real-time communications.
In this paper, we propose a pipeline to efficiently
compress LiDAR observations in an automotive scenario. First,
we leverage the capabilities of RangeNet++, a Deep Neural
Network (DNN) used to semantically infer point labels, to reduce
the channel load by selecting the most valuable environmental
data to be disseminated. Second, we compress the selected points
using Draco, a 3D compression algorithm which is able to obtain
compression up to the quantization error. Our experiments,
validated on the Semantic KITTI dataset, demonstrate that it
is possible to compress and send the information at the frame
rate of the LiDAR, thus achieving real-time performance.
Index Terms—Autonomous driving, compression, Draco, Deep
Neural Networks (DNNs), RangeNet++, point cloud, LiDAR.
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