Physics Informed Neural Fields for Smoke Reconstruction with Sparse Data

• URL: http://arxiv.org/abs/2206.06577v1
• Date: Tue, 14 Jun 2022 03:38:08 GMT
• Title: Physics Informed Neural Fields for Smoke Reconstruction with Sparse Data
• Authors: Mengyu Chu, Lingjie Liu, Quan Zheng, Erik Franz, Hans-Peter Seidel, Christian Theobalt, Rhaleb Zayer
• Abstract summary: High-fidelity reconstruction of fluids from sparse multiview RGB videos remains a formidable challenge. Existing solutions either assume knowledge of obstacles and lighting, or only focus on simple fluid scenes without obstacles or complex lighting. We present the first method to reconstruct dynamic fluid by leveraging the governing physics (ie, Navier -Stokes equations) in an end-to-end optimization.
• Score: 73.8970871148949
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: High-fidelity reconstruction of fluids from sparse multiview RGB videos remains a formidable challenge due to the complexity of the underlying physics as well as complex occlusion and lighting in captures. Existing solutions either assume knowledge of obstacles and lighting, or only focus on simple fluid scenes without obstacles or complex lighting, and thus are unsuitable for real-world scenes with unknown lighting or arbitrary obstacles. We present the first method to reconstruct dynamic fluid by leveraging the governing physics (ie, Navier -Stokes equations) in an end-to-end optimization from sparse videos without taking lighting conditions, geometry information, or boundary conditions as input. We provide a continuous spatio-temporal scene representation using neural networks as the ansatz of density and velocity solution functions for fluids as well as the radiance field for static objects. With a hybrid architecture that separates static and dynamic contents, fluid interactions with static obstacles are reconstructed for the first time without additional geometry input or human labeling. By augmenting time-varying neural radiance fields with physics-informed deep learning, our method benefits from the supervision of images and physical priors. To achieve robust optimization from sparse views, we introduced a layer-by-layer growing strategy to progressively increase the network capacity. Using progressively growing models with a new regularization term, we manage to disentangle density-color ambiguity in radiance fields without overfitting. A pretrained density-to-velocity fluid model is leveraged in addition as the data prior to avoid suboptimal velocity which underestimates vorticity but trivially fulfills physical equations. Our method exhibits high-quality results with relaxed constraints and strong flexibility on a representative set of synthetic and real flow captures.

Related papers

• Learning Physics From Video: Unsupervised Physical Parameter Estimation for Continuous Dynamical Systems [49.11170948406405]
  State-of-the-art in automatic parameter estimation from video is addressed by training supervised deep networks on large datasets. We propose a method to estimate the physical parameters of any known, continuous governing equation from single videos.
  arXiv  Detail & Related papers  (2024-10-02T09:44:54Z)
• Physics-Informed Learning of Characteristic Trajectories for Smoke Reconstruction [17.634226193457277]
  Existing physics-informed neural networks emphasize short-term physics constraints, leaving the proper preservation of long-term conservation less explored. We introduce Neural Characteristic Trajectory Fields, a novel representation utilizing Eulerian neural fields to implicitly model Lagrangian fluid trajectories. Building on the representation, we propose physics-informed trajectory learning and integration into NeRF-based scene reconstruction.
  arXiv  Detail & Related papers  (2024-07-12T20:19:41Z)
• NeAI: A Pre-convoluted Representation for Plug-and-Play Neural Ambient Illumination [28.433403714053103]
  We propose a framework named neural ambient illumination (NeAI) NeAI uses Neural Radiance Fields (NeRF) as a lighting model to handle complex lighting in a physically based way. Experiments demonstrate the superior performance of novel-view rendering compared to previous works.
  arXiv  Detail & Related papers  (2023-04-18T06:32:30Z)
• BLiRF: Bandlimited Radiance Fields for Dynamic Scene Modeling [43.246536947828844]
  We propose a framework that factorizes time and space by formulating a scene as a composition of bandlimited, high-dimensional signals. We demonstrate compelling results across complex dynamic scenes that involve changes in lighting, texture and long-range dynamics.
  arXiv  Detail & Related papers  (2023-02-27T06:40:32Z)
• Fast Dynamic Radiance Fields with Time-Aware Neural Voxels [106.69049089979433]
  We propose a radiance field framework by representing scenes with time-aware voxel features, named as TiNeuVox. Our framework accelerates the optimization of dynamic radiance fields while maintaining high rendering quality. Our TiNeuVox completes training with only 8 minutes and 8-MB storage cost while showing similar or even better rendering performance than previous dynamic NeRF methods.
  arXiv  Detail & Related papers  (2022-05-30T17:47:31Z)
• Learning Dynamic View Synthesis With Few RGBD Cameras [60.36357774688289]
  We propose to utilize RGBD cameras to synthesize free-viewpoint videos of dynamic indoor scenes. We generate point clouds from RGBD frames and then render them into free-viewpoint videos via a neural feature. We introduce a simple Regional Depth-Inpainting module that adaptively inpaints missing depth values to render complete novel views.
  arXiv  Detail & Related papers  (2022-04-22T03:17:35Z)
• Real-time Neural Radiance Caching for Path Tracing [67.46991813306708]
  We present a real-time neural radiance caching method for path-traced global illumination. Our system is designed to handle fully dynamic scenes, and makes no assumptions about the lighting, geometry, and materials. We demonstrate significant noise reduction at the cost of little induced bias, and report state-of-the-art, real-time performance on a number of challenging scenarios.
  arXiv  Detail & Related papers  (2021-06-23T13:09:58Z)
• Object-based Illumination Estimation with Rendering-aware Neural Networks [56.01734918693844]
  We present a scheme for fast environment light estimation from the RGBD appearance of individual objects and their local image areas. With the estimated lighting, virtual objects can be rendered in AR scenarios with shading that is consistent to the real scene.
  arXiv  Detail & Related papers  (2020-08-06T08:23:19Z)

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.