Results 11 to 20 of about 11,088 (289)
3D Point Cloud Compression [PDF]
The 24th International Conference on 3D Web Technology, 2019 In recent years, 3D point clouds have enjoyed a great popularity for representing both static and dynamic 3D objects. When compared to 3D meshes, they offer the advantage of providing a simpler, denser and more close-to-reality representation. However, point clouds always carry a huge amount of data.
Chao Cao, Marius Preda, Titus Zaharia
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TransPCGC: Point Cloud Geometry Compression Based on Transformers
Algorithms, 2023 Due to the often substantial size of the real-world point cloud data, efficient transmission and storage have become critical concerns. Point cloud compression plays a decisive role in addressing these challenges.
Shiyu Lu, Huamin Yang, Cheng Han
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Folding-Based Compression Of Point Cloud Attributes [PDF]
2020 IEEE International Conference on Image Processing (ICIP), 2020 Existing techniques to compress point cloud attributes leverage either geometric or video-based compression tools. We explore a radically different approach inspired by recent advances in point cloud representation learning. Point clouds can be interpreted as 2D manifolds in 3D space.
Quach, Maurice +2 more
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Point cloud data compression [PDF]
, 2023 The rapid growth in the popularity of Augmented Reality (AR), Virtual Reality (VR), and Mixed Reality (MR) experiences have resulted in an exponential surge of three-dimensional data. Point clouds have emerged as a commonly employed representation for capturing and visualizing three-dimensional data in these environments. Consequently, there has been a
Crespo Sagrado, Ismael +1 more
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Efficient Large-Scale Point Cloud Geometry Compression
SensorsDue to the significant bandwidth and memory requirements for transmitting and storing large-scale point clouds, considerable progress has been made in recent years in the field of large-scale point cloud geometry compression.
Shiyu Lu, Cheng Han, Huamin Yang
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Learned Point Cloud Geometry Compression
CoRR, 2019 This paper presents a novel end-to-end Learned Point Cloud Geometry Compression (a.k.a., Learned-PCGC) framework, to efficiently compress the point cloud geometry (PCG) using deep neural networks (DNN) based variational autoencoders (VAE). In our approach, PCG is first voxelized, scaled and partitioned into non-overlapped 3D cubes, which is then fed ...
Jianqiang Wang 0006 +5 more
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Predictive point-cloud compression [PDF]
ACM SIGGRAPH 2005 Sketches on - SIGGRAPH '05, 2005 Point clouds have recently become a popular alternative to polygonal meshes for representing three-dimensional geometric models. 3D photography and scanning systems acquire the geometry and appearance of real-world objects in form of point samples.
Stefan Gumhold +3 more
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Texture-Guided Graph Transform Optimization for Point Cloud Attribute Compression
Applied SciencesThere is a pressing need across various applications for efficiently compressing point clouds. While the Moving Picture Experts Group introduced the geometry-based point cloud compression (G-PCC) standard, its attribute compression scheme falls short of ...
Yiting Shao +4 more
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Multiscale Point Cloud Geometry Compression [PDF]
2021 Data Compression Conference (DCC), 2021 Recent years have witnessed the growth of point cloud based applications because of its realistic and fine-grained representation of 3D objects and scenes. However, it is a challenging problem to compress sparse, unstructured, and high-precision 3D points for efficient communication.
Jianqiang Wang 0006 +3 more
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Point cloud and particle data compression techniques [PDF]
, 2023 The contemporary need for heightened processing speed and storage capacity has necessitated the implementation of data compression in various applications.
Ravi, Niranjan
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