Results 61 to 70 of about 264,137 (214)

OctFormer: Efficient Octree-Based Transformer for Point Cloud Compression with Local Enhancement

AAAI Conference on Artificial Intelligence, 2023
Point cloud compression with a higher compression ratio and tiny loss is essential for efficient data transportation. However, previous methods that depend on 3D convolution or frequent multi-head self-attention operations bring huge computations.
Mingyue Cui, Junhua Long, Mingjian Feng, Boyang Li, Huang Kai   +4 more
semanticscholar   +1 more source

Star Formation in Transient Molecular Clouds

, 2003
We present the results of a numerical simulation in which star formation proceeds from an initially unbound molecular cloud core. The turbulent motions, which dominate the dynamics, dissipate in shocks leaving a quiescent region which becomes ...
Bonnell, Ian A., Clark, Paul C.
core   +2 more sources

Lidar Point Cloud Compression, Processing and Learning for Autonomous Driving

IEEE transactions on intelligent transportation systems (Print), 2023
As technology advances, cities are getting smarter. Smart mobility is the key element in smart cities and Autonomous Driving (AV) are an essential part of smart mobility.
R. Abbasi, A. Bashir, Hasan J. Alyamani, Farhan Amin, Jaehyeok Doh, Jianwen Chen   +5 more
semanticscholar   +1 more source

Radial Profiles of Star Formation in the Far Outer Regions of Galaxy Disks

, 2005
Star formation in galaxies is triggered by a combination of processes, including gravitational instabilities, spiral wave shocks, stellar compression, and turbulence compression.
Barteldrees A., Beijersbergen M., Bottema R., Bottema R., Boulanger F., Bruce G. Elmegreen, de Jong R. S., Deidre A. Hunter, Fall S. M., Naslund M., Pohlen M., Safronov V. S., Schwarzkopf U., Tenorio-Tagle G., van der Kruit P. C., van der Kruit P. C., van der Kruit P. C., Yoshii Y.   +17 more
core   +2 more sources

Point Cloud Compression via Constrained Optimal Transport [PDF]

IEEE International Conference on Multimedia and Expo
This paper presents a novel point cloud compression method COT-PCC by formulating the task as a constrained optimal transport (COT) problem. COT-PCC takes the bitrate of compressed features as an extra constraint of optimal transport (OT) which learns ...
Zezeng Li, Weimin Wang, Ziliang Wang, Na Lei   +3 more
semanticscholar   +1 more source

Compressive Point Cloud Super Resolution [PDF]

, 2012
Automatic target recognition (ATR) is the ability for a computer to discriminate between different objects in a scene. ATR is often performed on point cloud data from a sensor known as a Ladar. Increasing the resolution of this point cloud in order to get a more clear view of the object in a scene would be of significant interest in an ATR application.
openaire   +2 more sources

Collective oscillations of a Fermi gas in the unitarity limit: Temperature effects and the role of pair correlations

, 2008
We present detailed measurements of the frequency and damping of three different collective modes in an ultracold trapped Fermi gas of $^6$Li atoms with resonantly tuned interactions. The measurements are carried out over a wide range of temperatures. We
Altmeyer, A., Bruun, G. M., Denschlag, J. Hecker, Grimm, R., Guajardo, E. R. Sanchez, Kohstall, C., Riedl, S., Smith, H., Wright, M. J.   +8 more
core   +1 more source

Three-Dimensional Point Cloud Applications, Datasets, and Compression Methodologies for Remote Sensing: A Meta-Survey

Sensors
This meta-survey provides a comprehensive review of 3D point cloud (PC) applications in remote sensing (RS), essential datasets available for research and development purposes, and state-of-the-art point cloud compression methods.
Emil Dumic, Luís A. da Silva Cruz
doaj   +1 more source

On the Rapid Collapse and Evolution of Molecular Clouds

, 2007
Stars generally form faster than the ambipolar diffusion time, suggesting that several processes short circuit the delay and promote a rapid collapse.
Bacmann A., Ballesteros-Paredes J., Battinelli P., Bonnell I. A., Bruce G. Elmegreen, Di Fazio A., Efremov Y. N., Elmegreen B. G., Fukui Y., Goldreich P., Greaves J. S., Holland W. S., Honma M., Hujeirat A., Larson R. B., Mestel L., Motte F., Nakamura F., Nakano T., Omont A., Smith M. D., Stutzki J., Tilley D. A., Umebayashi T., Verschueren W., Whitworth A.   +25 more
core   +1 more source

Learned Point Cloud Geometry Compression

, 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 ...
Wang, Jianqiang, Zhu, Hao, Ma, Zhan, Chen, Tong, Liu, Haojie, Shen, Qiu   +5 more
openaire   +2 more sources