LiDAR Point Cloud Compression by Vertically Placed Objects Based on Global Motion Prediction

Kim, Junsik; Rhee, Seongbae; Kwon, Hyukmin; Kim, Kyuheon

doi:10.1109/access.2022.3148252

Cited by 9 publications

(3 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Inter-EM leverages these motions to achieve higher compression ratios. To further enhance the global motion estimation of Inter-EM, Kim et al [100] introduced a histogram-based point cloud classification that considers both vertically and horizontally positioned objects, in contrast to Inter-EM's single horizontal classification. The traditional Inter-EM approach evaluates the point distance d within octree nodes to detect changes (as depicted in Figure 10a).…”

Section: D Space Octreementioning

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

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.

show abstract

Section: D Space Octreementioning

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

“…A local property is determined by this macroblock motion. Local motion information may be used to and Lempel-Ziv-Welch (LZW) [96], [98]. The most promising schemes [97] provide accurate but efficient compression for point clouds.…”

Section: A Normalization Of 3dpc Compressionmentioning

confidence: 99%

Lidar Point Cloud Compression, Processing and Learning for Autonomous Driving

Abbasi

Alyamani

Amin

et al. 2023

IEEE Trans. Intell. Transport. Syst.

View full text Add to dashboard Cite

As technology advances, cities are getting smarter. Smart mobility is the key element in smart cities and Autonomous Driving (AV) are an essential part of smart mobility. However, the vulnerability of unmanned vehicles can also affect the value of life and human safety. In this paper, we provide a comprehensive analysis of 3D Point-Cloud (3DPC) processing and learning in terms of development, advancement, and performance for the AV system. 3DPC has recently attracted growing interest due to its extensive applications, such as autonomous driving, computer vision, and robotics. Light Detection and Ranging Sensors (LiDAR) is one of the most significant sensors in AV, which collects 3DPC that can accurately capture the outer surfaces of scenes and objects. Learning and processing tools in the 3DPC are essential for creating maps, perceptions, and localization devices in AV. The intention behind 3DPC learning and practical processing tools is to be considered the most essential modules to create, locate, and perceive maps in an AV system. The goal of the study is to know "what has been tested in AV system so far and what is necessary to make it safer and more practical in AV system." We also provide insights into the necessary open problems that are required to be resolved in the future.

show abstract

“…Notably, recent advancements have given rise to two standardized approaches within the Moving Picture Experts Group: video-based point cloud compression (V-PCC) and geometry-based point cloud compression (G-PCC) 1,2,4 . G-PCC, in particular, leverages data structures that excel in handling static scenarios, rendering it highly effective for addressing the requirements of both Category 1 and Category 3 of point clouds [14][15][16] . While the V-PCC exhibits superior performance compressing the dynamic scenes, making it the more suitable choice for Category 2 14,17,18 .…”

Section: Introductionmentioning

confidence: 99%

Improved video-based point cloud compression via segmentation

Tohidi,

Paul,

Ulhaq

et al. 2024

Preprint

View full text Add to dashboard Cite

Point cloud is a representation of objects or scenes utilising unordered points comprising 3D positions and attributes. The ability of point clouds to mimic natural forms has gained significant attention from diverse applied fields, such as virtual realityand augmented reality. However, the point cloud, especially the dynamic one, must be compressed efficiently due to its hugedata volume. The latest video-based point cloud compression (V-PCC) standard for dynamic point clouds divides the 3D pointcloud into many patches using computationally expensive normal estimation, segmentation, and refinement. The patches areprojected onto a 2D plane to apply existing video coding techniques. This process often results in losing proximity informationand some original points. This loss induces artefacts that adversely affect user perception. The proposed method segments dynamic point clouds based on shape similarity and occlusion before patch generation. This segmentation strategy helps tomaintain the proximity of the points and retain more original points by exploiting density and occlusion of the points . The experimental results establish that the proposed method significantly outperforms the V-PCC standard and other relevant methods in terms of rate-distortion performance and subjective quality testing for both geometric and texture data of several benchmark video sequences.

show abstract

LiDAR Point Cloud Compression by Vertically Placed Objects Based on Global Motion Prediction

Abstract: Date of publication xxxx 00, 0000, date of current version xxxx 00, 0000.

Cited by 9 publications

References 17 publications

A Survey on Data Compression Techniques for Automotive LiDAR Point Clouds

A Survey on Data Compression Techniques for Automotive LiDAR Point Clouds

Lidar Point Cloud Compression, Processing and Learning for Autonomous Driving

Improved video-based point cloud compression via segmentation

Contact Info

Product

Resources

About