Learned compression

@article{Mari,

title = {CACTUS: Content-Aware Compression and Transmission Using Semantics for Automotive LiDAR Data},

author = {Daniele Mari and Elena Camuffo and Simone Milani},

editor = {MDPI},

url = {https://www.mdpi.com/1424-8220/23/12/5611},

doi = {10.3390/s23125611},

year  = {2023},

date = {2023-06-15},

urldate = {2023-06-15},

journal = {Sensors},

volume = {23},

issue = {12},

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.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Camuffo2022,

title = {Recent advancements in learning algorithms for point clouds: An updated overview},

author = {Elena Camuffo and Daniele Mari and Simone Milani},

url = {https://www.mdpi.com/1424-8220/22/4/1357},

doi = {https://doi.org/10.3390/s22041357},

year  = {2022},

date = {2022-01-01},

urldate = {2022-01-01},

journal = {Sensors},

volume = {22},

number = {4},

pages = {1357},

publisher = {MDPI},

abstract = {Recent advancements in self-driving cars, robotics, and remote sensing have widened the range of applications for 3D Point Cloud (PC) data. This data format poses several new issues concerning noise levels, sparsity, and required storage space; as a result, many recent works address PC problems using Deep Learning (DL) solutions thanks to their capability to automatically extract features and achieve high performances. Such evolution has also changed the structure of processing chains and posed new problems to both academic and industrial researchers. The aim of this paper is to provide a comprehensive overview of the latest state-of-the-art DL approaches for the most crucial PC processing operations, i.e., semantic scene understanding, compression, and completion. With respect to the existing reviews, the work proposes a new taxonomical classification of the approaches, taking into account the characteristics of the acquisition set up, the peculiarities of the acquired PC data, the presence of side information (depending on the adopted dataset), the data formatting, and the characteristics of the DL architectures. This organization allows one to better comprehend some final performance comparisons on common test sets and cast a light on the future research trends.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Milani2020,

title = {A transform coding strategy for dynamic point clouds},

author = {Simone Milani and Enrico Polo and Simone Limuti},

year  = {2020},

date = {2020-01-01},

urldate = {2020-01-01},

journal = {IEEE Transactions on Image Processing},

volume = {29},

pages = {8213--8225},

publisher = {IEEE},

keywords = {},

pubstate = {published},

tppubtype = {article}

}