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Results for “neural radiance field”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sources NASA NTRS DOE OSTI

DOE OSTI2023

Evaluating Neural Radiance Fields for Commercial Satellite Video

We evaluate neural radiance fields (NeRFs) as a method for reconstructing 3D volumetric scenes from low Earth orbit satellite imagery. We leverage commercial satellite data to reconstruct a scene using existing software tools. In doing so, we identify difficulties in these mapping datasets for NeRF generation. We propose potential applications in geospatial intelligence for context and improved image interpretation.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2026

Applications Of Machine Learning to Gas Plume Analysis In Longwave Infrared Hyperspectral Images

Longwave infrared hyperspectral images can be used for gas plume analysis, as many gases exhibit distinct absorption features in this portion of the electromagnetic spectrum. In practice, accurately identifying weak gas signatures is difficult because the observed radiance is dominated by background radiance, which varies with material, temperature, and viewing conditions. Many gas plume analysis pipelines operate on single images, limiting the ability to leverage spatial and multi-view information that could enhance the analysis. The goal of this dissertation is to explore how machine learning and deep learning methods can complement classical approaches to improve gas plume identification in longwave infrared hyperspectral imagery, and to investigate the use of neural radiance fields for hyperspectral scene reconstruction.

3D Scene Reconstruction↗

DOE OSTI2023

Ultrasparse View X-ray Computed Tomography for 4D Imaging

X-ray computed tomography (CT) is a noninvasive, nondestructive approach to imaging materials, material systems, and engineered components in two and three dimensions. Acquisition of three-dimensional (3D) images requires the collection of hundreds or thousands of through-thickness X-ray radiographic images from different angles. Such 3D data acquisition strategies commonly involve suboptimal temporal sampling for in situ and operando studies (4D imaging). Herein, we introduce a sparse-view imaging approach, Tomo-NeRF, which is capable of reconstructing high-fidelity 3D images from <10 twodimensional radiographic images. Experimental 2D and 3D X-ray images were used to test the reconstruction capability in two-view, four-view, and six-view scenarios. Tomo-NeRF is capable of reconstructing 3D images with a structural similarity of 0.9971–0.9975 and a voxel-wise accuracy of 81.83–89.59% from 2D experimentally obtained images. Furthermore, the reconstruction accuracy for the experimentally obtained images is less than the synthetic structures. Experimentally obtained images demonstrate a similarity of 0.9973–0.9984 and a voxelwise accuracy of 84.31–95.77%.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗