Morphology Decoder: A Machine Learning Guided 3D Vision Quantifying Heterogenous Rock Permeability for Planetary Surveillance and Robotic Functions

• URL: http://arxiv.org/abs/2111.13460v1
• Date: Fri, 26 Nov 2021 12:20:03 GMT
• Title: Morphology Decoder: A Machine Learning Guided 3D Vision Quantifying Heterogenous Rock Permeability for Planetary Surveillance and Robotic Functions
• Authors: Omar Alfarisi, Aikifa Raza, Djamel Ouzzane, Hongxia Li, Mohamed Sassi, Tiejun Zhang
• Abstract summary: Permeability has a dominant influence on the flow properties of a natural fluid. Lattice Boltzmann simulator determines permeability from the nano and micropore network. We propose a morphology decoder, a parallel and serial flow reconstruction of machine learning segmented heterogeneous Cretaceous texture.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Permeability has a dominant influence on the flow properties of a natural fluid. Lattice Boltzmann simulator determines permeability from the nano and micropore network. The simulator holds millions of flow dynamics calculations with its accumulated errors and high consumption of computing power. To efficiently and consistently predict permeability, we propose a morphology decoder, a parallel and serial flow reconstruction of machine learning segmented heterogeneous Cretaceous texture from 3D micro computerized tomography and nuclear magnetic resonance images. For 3D vision, we introduce controllable-measurable-volume as new supervised segmentation, in which a unique set of voxel intensity corresponds to grain and pore throat sizes. The morphology decoder demarks and aggregates the morphologies boundaries in a novel way to produce permeability. Morphology decoder method consists of five novel processes, which describes in this paper, these novel processes are: (1) Geometrical 3D Permeability, (2) Machine Learning guided 3D Properties Recognition of Rock Morphology, (3) 3D Image Properties Integration Model for Permeability, (4) MRI Permeability Imager, and (5) Morphology Decoder (the process that integrates the other four novel processes).

Related papers

• DeforHMR: Vision Transformer with Deformable Cross-Attention for 3D Human Mesh Recovery [2.1653492349540784]
  DeforHMR is a novel regression-based monocular HMR framework designed to enhance the prediction of human pose parameters. DeforHMR leverages a novel query-agnostic deformable cross-attention mechanism within the transformer decoder. It achieves state-of-the-art performance for single-frame regression-based methods on the widely used 3D HMR benchmarks 3DPW and RICH.
  arXiv  Detail & Related papers  (2024-11-18T00:46:59Z)
• Equivariant Graph Neural Operator for Modeling 3D Dynamics [148.98826858078556]
  We propose Equivariant Graph Neural Operator (EGNO) to directly models dynamics as trajectories instead of just next-step prediction. EGNO explicitly learns the temporal evolution of 3D dynamics where we formulate the dynamics as a function over time and learn neural operators to approximate it. Comprehensive experiments in multiple domains, including particle simulations, human motion capture, and molecular dynamics, demonstrate the significantly superior performance of EGNO against existing methods.
  arXiv  Detail & Related papers  (2024-01-19T21:50:32Z)
• DiffuseBot: Breeding Soft Robots With Physics-Augmented Generative Diffusion Models [102.13968267347553]
  We present DiffuseBot, a physics-augmented diffusion model that generates soft robot morphologies capable of excelling in a wide spectrum of tasks. We showcase a range of simulated and fabricated robots along with their capabilities.
  arXiv  Detail & Related papers  (2023-11-28T18:58:48Z)
• Multi-view Hybrid Graph Convolutional Network for Volume-to-mesh Reconstruction in Cardiovascular MRI [43.47826598981827]
  We introduce HybridVNet, a novel architecture for direct image-to-mesh extraction. We show it can efficiently handle surface and volumetric meshes by encoding them as graph structures. Our model combines traditional convolutional networks with variational graph generative models, deep supervision and mesh-specific regularisation.
  arXiv  Detail & Related papers  (2023-11-22T21:51:29Z)
• GVP: Generative Volumetric Primitives [76.95231302205235]
  We present Generative Volumetric Primitives (GVP), the first pure 3D generative model that can sample and render 512-resolution images in real-time. GVP jointly models a number of primitives and their spatial information, both of which can be efficiently generated via a 2D convolutional network. Experiments on several datasets demonstrate superior efficiency and 3D consistency of GVP over the state-of-the-art.
  arXiv  Detail & Related papers  (2023-03-31T16:50:23Z)
• CryoAI: Amortized Inference of Poses for Ab Initio Reconstruction of 3D Molecular Volumes from Real Cryo-EM Images [30.738209997049395]
  We introduce cryoAI, an ab initio reconstruction algorithm for homogeneous conformations that uses gradient-based optimization of particle poses and the electron scattering potential from single-particle cryo-EM data. CryoAI achieves results on par with state-of-the-art cryo-EM solvers for both simulated and experimental data, one order of magnitude faster for large datasets and with significantly lower memory requirements than existing methods.
  arXiv  Detail & Related papers  (2022-03-15T17:58:03Z)
• 3D Reconstruction of Curvilinear Structures with Stereo Matching DeepConvolutional Neural Networks [52.710012864395246]
  We propose a fully automated pipeline for both detection and matching of curvilinear structures in stereo pairs. We mainly focus on 3D reconstruction of dislocations from stereo pairs of TEM images.
  arXiv  Detail & Related papers  (2021-10-14T23:05:47Z)
• Scene Synthesis via Uncertainty-Driven Attribute Synchronization [52.31834816911887]
  This paper introduces a novel neural scene synthesis approach that can capture diverse feature patterns of 3D scenes. Our method combines the strength of both neural network-based and conventional scene synthesis approaches.
  arXiv  Detail & Related papers  (2021-08-30T19:45:07Z)
• gradSim: Differentiable simulation for system identification and visuomotor control [66.37288629125996]
  We present gradSim, a framework that overcomes the dependence on 3D supervision by leveraging differentiable multiphysics simulation and differentiable rendering. Our unified graph enables learning in challenging visuomotor control tasks, without relying on state-based (3D) supervision.
  arXiv  Detail & Related papers  (2021-04-06T16:32:01Z)
• VC-Net: Deep Volume-Composition Networks for Segmentation and Visualization of Highly Sparse and Noisy Image Data [13.805816310795256]
  We present an end-to-end deep learning method, VC-Net, for robust extraction of 3D microvasculature. The core novelty is to automatically leverage the volume visualization technique (MIP) to enhance the 3D data exploration. A multi-stream convolutional neural network is proposed to learn the 3D volume and 2D MIP features respectively and then explore their inter-dependencies in a joint volume-composition embedding space.
  arXiv  Detail & Related papers  (2020-09-14T04:15:02Z)
• A new geodesic-based feature for characterization of 3D shapes: application to soft tissue organ temporal deformations [0.0]
  We show a direct application on a study of organ temporal deformations. We characterize the behavior of a bladder during a forced respiratory motion with a reduced number of 3D surface points. We demonstrate the robustness of our feature on both synthetic 3D shapes and realistic dynamic MRI data.
  arXiv  Detail & Related papers  (2020-03-18T16:56:41Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.