Results 11 to 20 of about 90,914 (289)
MPT: Mesh Pre-Training with Transformers for Human Pose and Mesh Reconstruction [PDF]
, 2023 Traditional methods of reconstructing 3D human pose and mesh from single images rely on paired image-mesh datasets, which can be difficult and expensive to obtain.
Lin, Kevin +4 more
core +1 more source
MeshingNet: A New Mesh Generation Method based on Deep Learning [PDF]
, 2020 We introduce a novel approach to automatic unstructured mesh generation using machine learning to predict an optimal finite element mesh for a previously unseen problem.
Wang, H, Wang, Y, Jimack, PK, Zhang, Z
core +1 more source
HyperU-Mesh: Real-time deformation of soft-tissues across variable patient-specific parameters
International audiencePhysics-based Patient-Specific Biomechanical models (PSBMs), particularly those using finite element methods (FEM), simulate organ behaviors accurately but are computationally intensive, especially for hyper-elastic tissues.
El Hadramy, Sidaty +2 more
core +6 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
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
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
DA Wand: Distortion-Aware Selection using Neural Mesh Parameterization [PDF]
, 2023 We present a neural technique for learning to select a local sub-region around a point which can be used for mesh parameterization. The motivation for our framework is driven by interactive workflows used for decaling, texturing, or painting on surfaces.
Hanocka, Rana +3 more
core +1 more source