Results 21 to 30 of about 7,665,845 (335)
Multiscale Point Cloud Geometry Compression [PDF]
Data Compression Conference, 2020 Recent years have witnessed the growth of point cloud based applications for both immersive media as well as 3D sensing for auto-driving, because of its realistic and fine-grained representation of 3D objects and scenes.
Jianqiang Wang +3 more
semanticscholar +1 more source
Multiscale deep context modeling for lossless point cloud geometry compression [PDF]
2021 IEEE International Conference on Multimedia & Expo Workshops (ICMEW), 2021 We propose a practical deep generative approach for lossless point cloud geometry compression, called MSVoxelDNN, and show that it significantly reduces the rate compared to the MPEG G-PCC codec. Our previous work based on autoregressive models (VoxelDNN
D. Nguyen +3 more
semanticscholar +1 more source
Modeling of TAE mode excitation with an antenna in realistic X-point geometry
Physics of Plasmas, 2020 Experimentally, it is observed that Toroidal Alfven Eigenmodes (TAEs) are difficult to excite with an external antenna when the plasma is in X-point geometry.
A. Dvornova +9 more
semanticscholar +1 more source
Lossless Coding of Point Cloud Geometry Using a Deep Generative Model [PDF]
IEEE transactions on circuits and systems for video technology (Print), 2021 This paper proposes a lossless point cloud (PC) geometry compression method that uses neural networks to estimate the probability distribution of voxel occupancy. First, to take into account the PC sparsity, our method adaptively partitions a point cloud
D. Nguyen +3 more
semanticscholar +1 more source
Layered Projection-Based Quality Assessment of 3D Point Clouds
IEEE Access, 2021 Point clouds are subject to various distortions during point cloud processing missions, any of which may lead to quality degradation. Consequently, predicting point cloud quality has attracted a lot of attention. In this paper, a layered projection-based
Tianxin Chen +7 more
doaj +1 more source
Patch-Based Deep Autoencoder for Point Cloud Geometry Compression [PDF]
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.
Kang-Soo You, Pan Gao
semanticscholar +1 more source
PU-Dense: Sparse Tensor-Based Point Cloud Geometry Upsampling
IEEE Transactions on Image Processing, 2022 Due to the increased popularity of augmented and virtual reality experiences, the interest in capturing high-resolution real-world point clouds has never been higher.
Anique Akhtar +4 more
semanticscholar +1 more source
Learning Geometry-Disentangled Representation for Complementary Understanding of 3D Object Point Cloud [PDF]
AAAI Conference on Artificial Intelligence, 2020 In 2D image processing, some attempts decompose images into high and low frequency components for describing edge and smooth parts respectively. Similarly, the contour and flat area of 3D objects, such as the boundary and seat area of a chair, describe ...
Mutian Xu +5 more
semanticscholar +1 more source
Pairwise transitive 2-designs [PDF]
, 2015 We classify the pairwise transitive 2-designs, that is, 2-designs such that a group of automorphisms is transitive on the following five sets of ordered pairs: point-pairs, incident point-block pairs, non-incident point-block pairs, intersecting block ...
Devillers, Alice, Praeger, Cheryl E.
core +1 more source
Geometry-Informed Neural Operator for Large-Scale 3D PDEs [PDF]
Neural Information Processing Systems, 2023 We propose the geometry-informed neural operator (GINO), a highly efficient approach to learning the solution operator of large-scale partial differential equations with varying geometries.
Zong-Yi Li +10 more
semanticscholar +1 more source