Neural rendering enables dynamic tomography

TL;DR

This paper introduces a neural rendering approach that enables real-time 3D tomography during dynamic experiments, overcoming limitations of traditional methods that require interruption.

Contribution

It presents a novel neural radiance field-based method for dynamic 3D reconstruction, including theoretical guidance and a spatio-temporal deformation model.

Findings

Neural radiance fields outperform conventional methods in data reconstruction efficiency.

The proposed model accurately captures real-time deformation in experiments.

Theoretical results support optimal projection angle selection.

Abstract

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.