Results 11 to 20 of about 85,010 (268)
Synthetic Training for Monocular Human Mesh Recovery
CoRR, 2020 Recovering 3D human mesh from monocular images is a popular topic in computer vision and has a wide range of applications. This paper aims to estimate 3D mesh of multiple body parts (e.g., body, hands) with large-scale differences from a single RGB image. Existing methods are mostly based on iterative optimization, which is very time-consuming.
Yu Sun 0030 +6 more
openaire +2 more sources
Spatially Adaptive Regularizer for Mesh Denoising
IEEE Access, 2020 Mesh denoising is a fundamental yet not well-solved problem in computer graphics. Many existing methods formulate the mesh denoising as an optimization problem, whereby the optimized mesh could best fit both the input and a set of constraints defined as ...
Xuan Cheng +4 more
doaj +1 more source
AppliedMath, 2023 As a class of non-Newtonian fluids with yield stresses, Bingham fluids possess both solid and liquid phases separated by implicitly defined non-physical yield surfaces, which makes the standard numerical discretization challenging.
Jianying Zhang
doaj +1 more source
Abdominal organ segmentation via deep diffeomorphic mesh deformations
Scientific Reports, 2023 Abdominal organ segmentation from CT and MRI is an essential prerequisite for surgical planning and computer-aided navigation systems. It is challenging due to the high variability in the shape, size, and position of abdominal organs.
Fabian Bongratz +2 more
doaj +1 more source
Highly Available Data Parallel ML training on Mesh Networks
CoRR, 2020 Data parallel ML models can take several days or weeks to train on several accelerators. The long duration of training relies on the cluster of resources to be available for the job to keep running for the entire duration. On a mesh network this is challenging because failures will create holes in the mesh.
Sameer Kumar, Norm Jouppi
openaire +2 more sources
Sensors, 2022 Since the 20th century, a rapid process of motorization has begun. The main goal of researchers, engineers and technology companies is to increase the safety and optimality of the movement of vehicles, as well as to reduce the environmental damage caused
Mikhail Gorodnichev +3 more
doaj +1 more source
SEMANTIC URBAN MESH ENHANCEMENT UTILIZING A HYBRID MODEL [PDF]
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2019 We propose a feature-based approach for semantic mesh segmentation in an urban scenario using real-world training data. There are only few works that deal with semantic interpretation of urban triangle meshes so far.
P. Tutzauer, D. Laupheimer, N. Haala
doaj +1 more source
Nihon Kikai Gakkai ronbunshu, 2023 In this paper, the prediction of crack propagation with two cracks using machine learning is described. The analysis results of crack propagation by s-version FEM (s-FEM), which combines the automatic mesh generation technique, are used for generation of
Rekisei OZAWA +3 more
doaj +1 more source
Broadband Communications for High Speed Trains via NDN Wireless Mesh Network [PDF]
Tsinghua Science and Technology, 2017 With increasing investment and deployment of high speed trains (HST), a critical demand of high bandwidth and the better user experience under high speed mobility arises. Although, TCP/IP has been studied in a static, walking and lower mobility environment, it can not work well at high speed (>50 m/s) cases.
Fan Wu 0014 +3 more
openaire +1 more source
, 2022 These learning resources (slides and tutorials) are related to the following training session:OpenFOAM advanced training.Moving meshes, rigid body motion, adaptive mesh refinement, and overset meshesThis training session was delivered in FEB2022 using OpenFOAM 9.The overset meshes training is based on OpenFOAM ESI (2020 or newer).The video lectures are
openaire +1 more source