Flame tomography is a compelling approach for extracting large amounts of data from experiments via 3-D thermochemical reconstruction. Recent efforts employing neural-network flame representations have suggested improved reconstruction quality compared with classical tomography approaches, but a rigorous quantitative comparison with the same algorithm using a voxel-grid representation has not been conducted. Here, we compare a classical voxel-grid representation with varying regularizers to a continuous neural representation for tomographic reconstruction of a simulated pool fire. The representations are constructed to give temperature and composition as a function of location, and a subsequent ray-tracing step is used to solve the radiative transfer equation to determine the spectral intensity incident on hyperspectral infrared cameras, which is then convolved with an instrument lineshape function. We demonstrate that the voxel-grid approach with a total-variation regularizer reproduces the ground-truth synthetic flame with the highest accuracy for reduced memory intensity and runtime. Future work will explore more representations and under experimental configurations.
Figures & tables
Figure 1: The tomographic process is conducted iteratively. (1) Temperature and composition are obtained by sampling the 3-D representation. We test continuous (Neural Network) and discrete (voxel) representations. (2) The radiative transfer equation is solved along each ray for spectral intensity Iη(s) , where s is the path length, η is the wavenumber, κη(T,X) is the temperature- and composition-dependent spectral absorption coefficient, and Ib,η(T) is the blackbody emission. (3) The Michelson spectral ILS (triangular apodization) is applied to the incident spectra. (4) The loss function is evaluated, (5) gradients with respect to representation parameters are computed, and (6) parameters are updated via gradient descent. This procedure is repeated until the predicted measurement g^ matches the ground truth g .
Figure 2: Cutaway plots of 3-D reconstructions from the DR pipeline against the ground truth. Isocontours of temperature are shown for intervals of 100 K between 500 and 1500 K.
Method
Epoch Time [ms]
Mem. [MB]
MSE T
MSE CO2
MSE H2O
VG/NR
430
3.0
0.0295
0.0436
0.0416
VG/Tikh.
432
3.0
0.0203
0.0391
0.0384
VG/TV
431
3.0
0.0121
0.0219
0.0197
NN/NR
479
65.0
0.0571
0.0871
0.2569
NN/Tikh.
489
66.0
0.0268
0.0705
0.0946
NN/TV
493
66.0
0.0754
0.1427
0.4356
Table 1: Performance comparison of different 3-D representations for the 3-D pool fire.
We propose a method to reconstruct dynamic fire in 3D from a limited set of camera views with a Gaussian-based spatiotemporal representation. Capturing and reconstructing fire and its dynamics is highly challenging due to its volatile nature, transparent quality, and multitude of high-frequency features. Despite these challenges, we aim to reconstruct fire from only three views, which consequently requires solving for under-constrained geometry. We solve this by separating the static background from the dynamic fire region by combining dense multi-view stereo images with monocular depth priors. The fire is initialized as a 3D flow field, obtained by fusing per-view dense optical flow projections. To capture the high frequency features of fire, each 3D Gaussian encodes a lifetime and linear velocity to match the dense optical flow. To ensure sub-frame temporal alignment across cameras we employ a custom hardware synchronization pattern -- allowing us to reconstruct fire with affordable commodity hardware. Our quantitative and qualitative validations across numerous reconstruction experiments demonstrate robust performance for diverse and challenging real fire scenarios.
Jakob Nazarenus, Dominik Michels, Wojtek Palubicki +4
Implicit neural representation (INR) methods provide continuous coordinate-to-value mappings and integrate naturally with direct volume rendering, making them attractive for representing volumetric data. However, existing INR-based approaches for volumetric data are inherently lossy, and even small reconstruction errors can propagate through rendering and downstream analysis. In this work, we explore Lossless-INR, a lossless INR framework for 3D scientific volumetric data based on bit-plane decomposition. By decomposing each voxel value into binary bit-planes, we reformulate reconstruction as per-bit binary classification, so that exact recovery reduces to predicting every bit correctly. To make this optimization tractable while keeping the representation compact, we combine an octree block-partitioning strategy that adaptively subdivides complex regions with a ternary feature-grid network whose grid entries are parameterized by a ternary set of values. Experiments on diverse volumetric datasets show that this design can achieve zero bit-error rate and bit-exact reconstruction, enabling faithful rendering and downstream analysis with a compact representation. The code is available at https://github.com/TouKaienn/Lossless-INR.
Hyperspectral images (HSI) have many applications, ranging from environmental monitoring to national security, and can be used for material detection and identification. Longwave infrared (LWIR) HSI can be used for gas plume detection and analysis. Oftentimes, only a few images of a scene of interest are available and are analyzed individually. The ability to combine information from multiple images into a single, cohesive representation could enhance analysis by providing more context on the scene's geometry and spectral properties. Neural radiance fields (NeRFs) create a latent neural representation of volumetric scene properties that enable novel-view rendering and geometry reconstruction, offering a promising avenue for hyperspectral 3D scene reconstruction. We explore the possibility of using NeRFs to create 3D scene reconstructions from LWIR HSI and demonstrate that the model can be used for the basic downstream analysis task of gas plume detection. The physics-based DIRSIG software suite was used to generate a synthetic multi-view LWIR HSI dataset of a simple facility with a strong sulfur hexafluoride gas plume. Our method, built on the standard Mip-NeRF architecture, combines state-of-the-art methods for hyperspectral NeRFs and sparse-view NeRFs, along with a novel adaptive weighted MSE loss. Our final NeRF method requires around 50% fewer training images than the standard Mip-NeRF and achieves an average PSNR of 39.8 dB with as few as 30 training images. Gas plume detection applied to NeRF-rendered test images using the adaptive coherence estimator achieves an average AUC of 0.821 when compared with detection masks generated from ground-truth test images.
Scout Jarman, Zigfried Hampel-Arias, Adra Carr +1
Utah State University, Department of Mathematics & Statistics, Logan, UT USA · Los Alamos National Laboratory, Intelligence and Space Research Division, Los Alamos, NM USA