Results 81 to 90 of about 264,137 (214)

msLPCC: A Multimodal-Driven Scalable Framework for Deep LiDAR Point Cloud Compression

AAAI Conference on Artificial Intelligence
LiDAR sensors are widely used in autonomous driving, and the growing storage and transmission demands have made LiDAR point cloud compression (LPCC) a hot research topic. To address the challenges posed by the large-scale and uneven-distribution (spatial
Miaohui Wang, Runnan Huang, H. Dong, Di Lin, Yun Song, Wuyuan Xie   +5 more
semanticscholar   +1 more source

VOXEL Video Streaming Over Wireless Networks

Iraqi Journal of Information & Communication Technology
High-density point clouds expressing attractive 3D images are attracting attention. These gigantic media require large bandwidth allocations, making them problematic to stream to resource-constrained hand-held devices.
Estabraq Makiyah, Nassr N. Khamees
doaj   +1 more source

ANFPCGC++: Point Cloud Geometry Coding Using Augmented Normalizing Flows and Transformer-Based Entropy Model

IEEE Access
As immersive media gains increasing prominence, point clouds have emerged as a preferred data representation for presenting complex 3D scenes. However, the large size of point cloud data poses challenges in terms of storage and real-time transmission ...
Jui-Chiu Chiang, Ji-Jin Chiu, Monyneath Yim   +2 more
doaj   +1 more source

Scalable Human-Machine Point Cloud Compression [PDF]

Picture Coding Symposium
Due to the limited computational capabilities of edge devices, deep learning inference can be quite expensive. One remedy is to compress and transmit point cloud data over the network for server-side processing.
Mateen Ulhaq, Ivan V. Baji'c
semanticscholar   +1 more source

Guest editorial: Deep learning‐based point cloud processing, compression and analysis

Electronics Letters
Point cloud data is a large collection of high dimensional 3D points with 3D coordinates and attributes, which has been one of the mainstream representations for emerging 3D applications, such as virtual reality, autonomous vehicles, and robotics. Due to
Yun Zhang, Raouf Hamzaoui, Xu Wang, Junhui Hou, Giuseppe Valenzise   +4 more
doaj   +1 more source

Energy-Saving Geospatial Data Storage—LiDAR Point Cloud Compression

Energies
In recent years, the growth of digital data has been unimaginable. This also applies to geospatial data. One of the largest data types is LiDAR point clouds.
Artur Warchoł, Karolina Pęzioł, Marek Baścik   +2 more
doaj   +1 more source

PCAC-GAN: A sparse-tensor-based generative adversarial network for 3D point cloud attribute compression

Computational Visual Media
Learning-based methods have proven successful in compressing geometric information for point clouds. For attribute compression, however, they still lag behind non-learning-based methods such as the MPEG G-PCC standard.
Xiaolong Mao, Hui Yuan, Xin Lu, Raouf Hamzaoui, Wei Gao   +4 more
doaj   +1 more source

Point cloud geometry compression based on the combination of interlayer residual and IRN concatenated residual

Graphical Models
Point clouds have been attracting more and more attentions due to its capability of representing objects precisely, such as autonomous vehicle navigation, VR/AR, cultural heritage protection, etc.
Meng Huang, Qian Xu, Wenxuan Xu
doaj   +1 more source

Color Reduction in an Authenticate Live 3D Point Cloud Video Streaming System

Computers, 2016
In this paper, an authenticate live 3D point cloud video streaming system is presented, using a low cost 3D sensor camera, the Microsoft Kinect. The proposed system is implemented on a client-server network infrastructure.
Zainab Sultani, Rana F. Al-Tuma, Sandro Wefel   +2 more
doaj   +1 more source

Point Cloud Processing and Compression [PDF]

IEEE Transactions on Circuits and Systems for Video Technology, 2020
openaire   +3 more sources