Real-Time LiDAR Point Cloud Compression Using Bi-Directional Prediction and Range-Adaptive Floating-Point Coding

Zhao, Lili; Ma, Kai-Kuang; Lin, Xuhu; Wang, Wenyi; Chen, Jianwen

doi:10.1109/tbc.2022.3162406

Cited by 10 publications

(2 citation statements)

References 45 publications

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“…Finally, the B-frames can use information from both previous and future frames to predict and encode the changes. Focused on this last frame type, Zhao et al [90] proposed a bi-directional frame prediction network for inter-frame prediction followed by a 32-bit high-precision floating-point lossy encoder to compress the I-frames and B-frames. Some mapping applications also propose algorithms to compress spatially and temporally point clouds.…”

Section: Inter-frame Compressionmentioning

confidence: 99%

A Survey on Data Compression Techniques for Automotive LiDAR Point Clouds

Roriz,

Silva,

Dias

et al. 2024

Sensors

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In the evolving landscape of autonomous driving technology, Light Detection and Ranging (LiDAR) sensors have emerged as a pivotal instrument for enhancing environmental perception. They can offer precise, high-resolution, real-time 3D representations around a vehicle, and the ability for long-range measurements under low-light conditions. However, these advantages come at the cost of the large volume of data generated by the sensor, leading to several challenges in transmission, processing, and storage operations, which can be currently mitigated by employing data compression techniques to the point cloud. This article presents a survey of existing methods used to compress point cloud data for automotive LiDAR sensors. It presents a comprehensive taxonomy that categorizes these approaches into four main groups, comparing and discussing them across several important metrics.

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Section: Inter-frame Compressionmentioning

confidence: 99%

A Survey on Data Compression Techniques for Automotive LiDAR Point Clouds

Roriz,

Silva,

Dias

et al. 2024

Sensors

View full text Add to dashboard Cite

show abstract

“…In the first set of approaches, a compact representation of the geometry is obtained by using an ad hoc data structure (e.g., a graph, a KD-tree [ 51 ], an octree [ 15 ] or cellular automata [ 52 ]) and then entropy coding. Some recent solutions aim at exploiting the temporal redundancy existing between temporally adjacent acquisitions by entailing a bidirectional prediction [ 53 ].…”

Section: Related Workmentioning

confidence: 99%

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

Mari

Camuffo

Milani

2023

Sensors

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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.

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Lightweight fully connected network-based fast CU size decision for video-based point cloud compression

Que,

2023

Computers & Graphics

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Real-Time LiDAR Point Cloud Compression Using Bi-Directional Prediction and Range-Adaptive Floating-Point Coding

Cited by 10 publications

References 45 publications

A Survey on Data Compression Techniques for Automotive LiDAR Point Clouds

A Survey on Data Compression Techniques for Automotive LiDAR Point Clouds

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

Lightweight fully connected network-based fast CU size decision for video-based point cloud compression

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