Neural rendering enables dynamic tomography

• Main Authors: Grega, Ivan, Whitney, William F, Deshpande, Vikram S
• Format: Journal Article
• Language: English
• Published: 27-10-2024
• Subjects: Computer Science - Computer Vision and Pattern Recognition; Computer Science - Learning; Physics - Instrumentation and Detectors; Physics - Materials Science
• DOI: 10.48550/arxiv.2410.20558

Interrupted X-ray computed tomography (X-CT) has been the common way to
observe the deformation of materials during an experiment. While this approach
is effective for quasi-static experiments, it has never been possible to
reconstruct a full 3d tomography during a dynamic experiment which cannot be
interrupted. In this work, we propose that neural rendering tools can be used
to drive the paradigm shift to enable 3d reconstruction during dynamic events.
First, we derive theoretical results to support the selection of projections
angles. Via a combination of synthetic and experimental data, we demonstrate
that neural radiance fields can reconstruct data modalities of interest more
efficiently than conventional reconstruction methods. Finally, we develop a
spatio-temporal model with spline-based deformation field and demonstrate that
such model can reconstruct the spatio-temporal deformation of lattice samples
in real-world experiments.