Results 21 to 30 of about 11,088 (289)
LiDAR Point Cloud Compression by Vertically Placed Objects Based on Global Motion Prediction
IEEE Access, 2022 A point cloud acquired through a Light Detection And Ranging (LiDAR) sensor can be illustrated as a continuous frame with a time axis. Since the frame-by-frame point cloud has a high correlation between frames, a higher compression efficiency can be ...
Junsik Kim +3 more
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Motion Analysis and Performance Improved Method for 3D LiDAR Sensor Data Compression [PDF]
, 2021 Continuous point cloud data is being used more and more widely in practical applications such as mapping, localization and object detection in autonomous driving systems, but due to the huge volume of data involved, sharing and storing this data is ...
Miyajima, Chiyomi +4 more
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3D point cloud lossy compression using quadric surfaces [PDF]
PeerJ Computer Science, 2021 The presence of 3D sensors in hand-held or head-mounted smart devices has motivated many researchers around the globe to devise algorithms to manage 3D point cloud data efficiently and economically. This paper presents a novel lossy compression technique
Ulfat Imdad +3 more
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Patch Re-Segmentation and Packing for Dynamic Point Cloud Compression via Back-and-Forth Structure
IEEE Open Journal of Signal Processing, 2022 The dynamic point cloud is widely needed in 3D vision related applications such as virtual reality and telepresence. Due to the huge amount of data, a key technology before the effective application is the dynamic point cloud compression.
Haoyu Shi, Fan Li
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Prioritized Transmission Control of Point Cloud Data Obtained by LIDAR Devices
IEEE Access, 2020 Smart monitoring, particularly at intersections, is a promising service that is being considered for the concept of smart cities. A network of light detection and ranging (LIDAR) sensors, which generates point cloud data in real time, can be used to ...
Keiichiro Sato +6 more
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Learned Point Cloud Compression for Classification
2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), 2023 Deep learning is increasingly being used to perform machine vision tasks such as classification, object detection, and segmentation on 3D point cloud data. However, deep learning inference is computationally expensive. The limited computational capabilities of end devices thus necessitate a codec for transmitting point cloud data over the network for ...
Mateen Ulhaq, Ivan V. Bajic
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Cylindrical Coordinates for Lidar Point Cloud Compression [PDF]
2021 IEEE International Conference on Image Processing (ICIP), 2021 We present an efficient voxelization method to encode the geometry and attributes of 3D point clouds obtained from autonomous vehicles. Due to the circular scanning trajectory of sensors, the geometry of LiDAR point clouds is inherently different from that of point clouds captured from RGBD cameras.
Shashank N. Sridhara +2 more
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Geometric 3D point cloud compression [PDF]
Pattern Recognition Letters, 2014 This work has been supported by the Spanish Government DPI2013-40534-R grant.
Vicente Morell +3 more
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Video-Based Compression for Plenoptic Point Clouds [PDF]
2020 Data Compression Conference (DCC), 2020 10 pages, 4 ...
Li Li 0040 +3 more
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An Efficient LiDAR Point Cloud Map Coding Scheme Based on Segmentation and Frame-Inserting Network
Sensors, 2022 In this article, we present an efficient coding scheme for LiDAR point cloud maps. As a point cloud map consists of numerous single scans spliced together, by recording the time stamp and quaternion matrix of each scan during map building, we cast the ...
Qiang Wang +5 more
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