EM-GANSim: Real-time and Accurate EM Simulation Using Conditional GANs for 3D Indoor Scenes

• URL: http://arxiv.org/abs/2405.17366v1
• Date: Mon, 27 May 2024 17:19:02 GMT
• Title: EM-GANSim: Real-time and Accurate EM Simulation Using Conditional GANs for 3D Indoor Scenes
• Authors: Ruichen Wang, Dinesh Manocha,
• Abstract summary: We present a novel machine-learning (ML) approach (EM-GANSim) for real-time electromagnetic (EM) propagation. In practice, it can compute the signal strength in a few milliseconds on any location in 3D indoor environments.
• Score: 55.2480439325792
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: We present a novel machine-learning (ML) approach (EM-GANSim) for real-time electromagnetic (EM) propagation that is used for wireless communication simulation in 3D indoor environments. Our approach uses a modified conditional Generative Adversarial Network (GAN) that incorporates encoded geometry and transmitter location while adhering to the electromagnetic propagation theory. The overall physically-inspired learning is able to predict the power distribution in 3D scenes, which is represented using heatmaps. Our overall accuracy is comparable to ray tracing-based EM simulation, as evidenced by lower mean squared error values. Furthermore, our GAN-based method drastically reduces the computation time, achieving a 5X speedup on complex benchmarks. In practice, it can compute the signal strength in a few milliseconds on any location in 3D indoor environments. We also present a large dataset of 3D models and EM ray tracing-simulated heatmaps. To the best of our knowledge, EM-GANSim is the first real-time algorithm for EM simulation in complex 3D indoor environments. We plan to release the code and the dataset.

Related papers

• A Lightweight Human Pose Estimation Approach for Edge Computing-Enabled Metaverse with Compressive Sensing [23.884862152830184]
  The ability to estimate 3D movements of users over edge computing-enabled networks, such as 5G/6G networks, is a key enabler for the new era of extended reality (XR) and Metaverse applications. Recent advancements in deep learning have shown advantages over optimization techniques for estimating 3D human poses given spare measurements from sensor signals. We propose a novel approach for redundancy removal and lightweight transmission of IMU signals over noisy wireless environments.
  arXiv  Detail & Related papers  (2024-08-26T02:57:23Z)
• MM3DGS SLAM: Multi-modal 3D Gaussian Splatting for SLAM Using Vision, Depth, and Inertial Measurements [59.70107451308687]
  We show for the first time that using 3D Gaussians for map representation with unposed camera images and inertial measurements can enable accurate SLAM. Our method, MM3DGS, addresses the limitations of prior rendering by enabling faster scale awareness, and improved trajectory tracking. We also release a multi-modal dataset, UT-MM, collected from a mobile robot equipped with a camera and an inertial measurement unit.
  arXiv  Detail & Related papers  (2024-04-01T04:57:41Z)
• Efficient Physics-Based Learned Reconstruction Methods for Real-Time 3D Near-Field MIMO Radar Imaging [0.0]
  Near-field multiple-input multiple-output (MIMO) radar imaging systems have recently gained significant attention. In this paper, we develop novel non-iterative deep learning-based reconstruction methods for real-time near-field imaging. The goal is to achieve high image quality with low computational cost at settings.
  arXiv  Detail & Related papers  (2023-12-28T11:05:36Z)
• GIR: 3D Gaussian Inverse Rendering for Relightable Scene Factorization [62.13932669494098]
  This paper presents a 3D Gaussian Inverse Rendering (GIR) method, employing 3D Gaussian representations to factorize the scene into material properties, light, and geometry. We compute the normal of each 3D Gaussian using the shortest eigenvector, with a directional masking scheme forcing accurate normal estimation without external supervision. We adopt an efficient voxel-based indirect illumination tracing scheme that stores direction-aware outgoing radiance in each 3D Gaussian to disentangle secondary illumination for approximating multi-bounce light transport.
  arXiv  Detail & Related papers  (2023-12-08T16:05:15Z)
• Geometry-Informed Neural Operator for Large-Scale 3D PDEs [76.06115572844882]
  We propose the geometry-informed neural operator (GINO) to learn the solution operator of large-scale partial differential equations. We successfully trained GINO to predict the pressure on car surfaces using only five hundred data points.
  arXiv  Detail & Related papers  (2023-09-01T16:59:21Z)
• 3D Mitochondria Instance Segmentation with Spatio-Temporal Transformers [101.44668514239959]
  We propose a hybrid encoder-decoder framework that efficiently computes spatial and temporal attentions in parallel. We also introduce a semantic clutter-background adversarial loss during training that aids in the region of mitochondria instances from the background.
  arXiv  Detail & Related papers  (2023-03-21T17:58:49Z)
• Towards Real Time Thermal Simulations for Design Optimization using Graph Neural Networks [0.0]
  We present a method to simulate the thermal behavior of 3D systems using a graph neural network. The accuracy of the network result for one-step predictions is remarkable.
  arXiv  Detail & Related papers  (2022-09-21T13:48:41Z)
• Data-Driven Shadowgraph Simulation of a 3D Object [50.591267188664666]
  We are replacing the numerical code by a computationally cheaper projection based surrogate model. The model is able to approximate the electric fields at a given time without computing all preceding electric fields as required by numerical methods. This model has shown a good quality reconstruction in a problem of perturbation of data within a narrow range of simulation parameters and can be used for input data of large size.
  arXiv  Detail & Related papers  (2021-06-01T08:46:04Z)
• Physics Validation of Novel Convolutional 2D Architectures for Speeding Up High Energy Physics Simulations [0.0]
  We apply Geneversarative Adrial Networks (GANs), a deep learning technique, to replace the calorimeter detector simulations. We develop new two-dimensional convolutional networks to solve the same 3D image generation problem faster. Our results demonstrate a high physics accuracy and further consolidate the use of GANs for fast detector simulations.
  arXiv  Detail & Related papers  (2021-05-19T07:24:23Z)

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.