Distributed Deep Point Cloud Feature Compression for Vehicle-to-Vehicle Cooperative Perception

With the rapid development of autonomous driving, intermediate fusion cooperative perception has been proposed to broaden the sensing range of a single vehicle by fusing the point cloud features transmitted from surrounding vehicles. However, transmission of these features demands substantial bandwidth, necessitating the creation of efficient methods for compressing point cloud features. Our basic compression idea is to remove two types of semantic redundancies in Vehicle-to-Vehicle (V2V) cooperative perception scenarios: inter-redundancy between the transmitted features and the ego feature, and intra-redundancy among the symbols of transmitted features. To this end, we propose a Distributed deep Point cloud Feature Compression (DPFC) scheme with rate-perception optimization. Different from previous schemes that merely reduce the dimension of transmitted features using an autoencoder, we design a distributed feature compression network conditioned on the ego feature to remove inter-redundancy. To further remove intra-redundancy, we introduce rate-perception optimization to exploit in-depth spatial redundancy and symbol redundancy. Experimental results demonstrate that our proposed DPFC can significantly reduce the bandwidth cost while achieving higher perceptive performance compared with the autoencoder.