CACTUS: Content-Aware Compression and Transmission Using Semantics for Automotive LiDAR Data

Abstract

Many recent cloud or edge computing strategies for automotive applications require transmitting huge amounts of Light Detection and Ranging (LiDAR) data from terminals to centralized processing units. As a matter of fact, the development of effective Point Cloud (PC) compression strategies that preserve semantic information, which is critical for scene understanding, proves to be crucial. Segmentation and compression have always been treated as two independent tasks; however, since not all the semantic classes are equally important for the end task, this information can be used to guide data transmission. In this paper, we propose Content-Aware Compression and Transmission Using Semantics (CACTUS), which is a coding framework that exploits semantic information to optimize the data transmission, partitioning the original point set into separate data streams. Experimental results show that differently from traditional strategies, the independent coding of semantically consistent point sets preserves class information. Additionally, whenever semantic information needs to be transmitted to the receiver, using the CACTUS strategy leads to gains in terms of compression efficiency, and more in general, it improves the speed and flexibility of the baseline codec used to compress the data.

A Survey on Data Compression Techniques for Automotive LiDAR Point Clouds

“…Therefore, Wiesmann et al proposed a decoder that uses continuous 3D deconvolutions to recover the points based on learned features. Mari et al presented a framework [119] that exploits semantic information within point clouds to optimize data transmission. Similarly to other semantic-based methods, they first segment data into separate streams.…”