mlsanityData to 7 Oct 2026

Resistance-Time Co-Modulated PointNet for Temporal Super-Resolution Simulation of Blood Vessel Flows

arXiv 2111.10372 ↗First posted 19 Nov 2021eess.IV cs.CVcited by 0

Not on the map. Why?

Abstract (arXiv)

In this paper, a novel deep learning framework is proposed for temporal super-resolution simulation of blood vessel flows, in which a high-temporal-resolution time-varying blood vessel flow simulation is generated from a low-temporal-resolution flow simulation result. In our framework, point-cloud is used to represent the complex blood vessel model, resistance-time aided PointNet model is proposed for extracting the time-space features of the time-varying flow field, and finally we can reconstruct the high-accuracy and high-resolution flow field through the Decoder module. In particular, the amplitude loss and the orientation loss of the velocity are proposed from the vector characteristics of the velocity. And the combination of these two metrics constitutes the final loss function for network training. Several examples are given to illustrate the effective and efficiency of the proposed framework for temporal super-resolution simulation of blood vessel flows.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 16 of its 33 references that are in the map. First posted on arXiv 19 Nov 2021.

Builds on

16 of 33 references.

References · 16

  1. 2016PointNet: Deep Learning on Point Sets for 3D Classification and Segmentationcited by 5,223
  2. 2017PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Spacecited by 4,384
  3. 2017Progressive Growing of GANs for Improved Quality, Stability, and Variationcited by 2,967
  4. 2016Perceptual Losses for Real-Time Style Transfer and Super-Resolutioncited by 2,678
  5. 2016A Point Set Generation Network for 3D Object Reconstruction from a Single Imagecited by 972
  6. 2018PointCNN: Convolution On X-Transformed Pointscited by 764
  7. 2021Machine learning accelerated computational fluid dynamicscited by 461
  8. 2020PF-Net: Point Fractal Network for 3D Point Cloud Completioncited by 146
  9. 2016Accelerating Eulerian Fluid Simulation With Convolutional Networkscited by 128
  10. 2018Deep Fluids: A Generative Network for Parameterized Fluid Simulationscited by 121
  11. 2018Latent-space Physics: Towards Learning the Temporal Evolution of Fluid Flowcited by 97
  12. 2018tempoGAN: A Temporally Coherent, Volumetric GAN for Super-resolution Fluid Flowcited by 44
4 more
  1. 2018Learning to Synthesize Motion Blurcited by 37
  2. 2020Latent Space Subdivision: Stable and Controllable Time Predictions for Fluid Flowcited by 13
  3. 2016Interpolations of Smoke and Liquid Simulationscited by 3
  4. 2020SceneEncoder: Scene-Aware Semantic Segmentation of Point Clouds with A Learnable Scene Descriptorcited by 2

Built on by

Not cited yet.