用神经渲染实现动态实验中的三维重建。
Neural rendering enables dynamic tomography
- 结合合成与真实数据，用神经辐射场提升重建效率。
- 在真实实验中实现了晶格样品的时空变形实时重建。
- 适合材料动态行为研究、需要连续观测的实验场景。
中断式X射线计算机断层扫描（X-CT）常用于观察实验中材料的形变。尽管该方法在准静态实验中有效，但无法在不可中断的动态实验中完成完整三维重建。本文提出利用神经渲染技术推动范式转变，实现动态事件中的三维重建。首先，推导理论结果以优化投影角度选择；通过合成与实验数据结合，证明神经辐射场（Neural Radiance Fields）在重建目标数据模态方面优于传统方法；最后，构建基于样条的时空变形模型，成功实现真实实验中晶格样品的时空形变重建。
原文摘要 · Abstract (English)
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.
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