Results 31 to 40 of about 3,061 (275)
Deep Learning-based Point Cloud Geometry Coding with Resolution Scalability
, 2020 EISBN - 978-1-7281-9320-5Article number - 9287060; Conference date - 21 September 2020 - 24 September 2020; Conference code - 165866Point clouds are a 3D visual representation format that has recently become fundamentally important for immersive and ...
Guarda, André F. R. +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.
Gururajan, Ashwin Kumar
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Patch-Based Deep Autoencoder for Point Cloud Geometry Compression
ACM Multimedia Asia, 2021 The ever-increasing 3D application makes the point cloud compression unprecedentedly important and needed. In this paper, we propose a patch-based compression process using deep learning, focusing on the lossy point cloud geometry compression. Unlike existing point cloud compression networks, which apply feature extraction and reconstruction on the ...
Kang You, Pan Gao 0001
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Refining The Bounding Volumes for Lossless Compression of Voxelized Point Clouds Geometry [PDF]
2021 IEEE International Conference on Image Processing (ICIP), 2021 This paper describes a novel lossless compression method for point cloud geometry, building on a recent lossy compression method that aimed at reconstructing only the bounding volume of a point cloud. The proposed scheme starts by partially reconstructing the geometry from the two depthmaps associated to a single projection direction.
Emre Can Kaya +2 more
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Point AE-DCGAN: A deep learning model for 3D point cloud lossy geometry compression
, 2021 Xu J, Fang Z, Gao Y, et al. Point AE-DCGAN: A deep learning model for 3D point cloud lossy geometry compression. In: Bilgin A, Marcellin MW, Serra-Sagrista J, Storer JA, eds. 2021 Data Compression Conference (DCC).
Wang, Anjie +10 more
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LVAC: Learned volumetric attribute compression for point clouds using coordinate based networks
Frontiers in Signal Processing, 2022 We consider the attributes of a point cloud as samples of a vector-valued volumetric function at discrete positions. To compress the attributes given the positions, we compress the parameters of the volumetric function.
Berivan Isik +4 more
doaj +1 more source
Dynamic Point Cloud Geometry Compression using Cuboid based Commonality Modeling Framework
, 2021 Dynamic Point Cloud Geometry Compression using Cuboid based Commonality Modeling ...
Ashek Ahmmed (15838217) +3 more
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Inactive Region Filling Method for Efficient Compression Using Reinforcement Learning
IEEE Access, 2023 Based on the massive advancements in modern hardware technologies, beyond high-resolution images, immersive and interactive videos have become an important next-generation technology.
Dongsin Kim, Kutub Uddin, Byung Tae Oh
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IEEE AccessAs 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 +2 more
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A Lightweight Procedural Layer for Hybrid Experimental–Computational Workflows in Materials Science
Advanced Engineering Materials, EarlyView.We unveil a prototype hybrid‐workflow framework that fuses automatedcomputation with hands‐on experiments. Built atop pyiron, a lightweight, parameterized layer translates procedure descriptions into executable manual steps, syncing instrument settings, human interventions, and data capture in real‐time today.
Steffen Brinckmann +8 more
wiley +1 more source