Snapshot Compressive Imaging (SCI) offers a possibility for capturing information in high-speed dynamic scenes, requiring efficient reconstruction method to recover scene information. Despite promising results, current deep learning-based and NeRF-based reconstruction methods face challenges: 1) deep learning-based reconstruction methods struggle to maintain 3D structural consistency within scenes, and 2) NeRF-based reconstruction methods still face limitations in handling dynamic scenes. To address these challenges, we propose SCIGS, a variant of 3DGS, and develop a primitive-level transformation network that utilizes camera pose stamps and Gaussian primitive coordinates as embedding vectors. This approach resolves the necessity of camera pose in vanilla 3DGS and enhances multi-view 3D structural consistency in dynamic scenes by utilizing transformed primitives. Additionally, a high-frequency filter is introduced to eliminate the artifacts generated during the transformation. The proposed SCIGS is the first to reconstruct a 3D explicit scene from a single compressed image, extending its application to dynamic 3D scenes. Experiments on both static and dynamic scenes demonstrate that SCIGS not only enhances SCI decoding but also outperforms current state-of-the-art methods in reconstructing dynamic 3D scenes from a single compressed image.
Snapshot Compressive Imaging (SCI) 为捕捉高速动态场景信息提供了一种可能性，但需要高效的重建方法来恢复场景信息。尽管当前基于深度学习和 NeRF 的重建方法已取得一定进展，但仍存在以下挑战：1）基于深度学习的重建方法难以在场景中保持 3D 结构的一致性；2）基于 NeRF 的重建方法在处理动态场景时仍存在局限性。 为解决这些问题，我们提出了 SCIGS，这是 3D Gaussian Splatting (3DGS) 的一种变体，并开发了一个基元级变换网络。该网络利用相机姿态标记和高斯基元坐标作为嵌入向量，解决了传统 3DGS 中对相机姿态的依赖，同时通过变换后的基元增强动态场景中多视角 3D 结构的一致性。此外，我们引入了一个高频滤波器，消除变换过程中产生的伪影。 SCIGS 是首个能够从单张压缩图像重建 3D 显式场景的方法，并将其应用扩展至动态 3D 场景。对静态和动态场景的实验表明，SCIGS 不仅提升了 SCI 的解码能力，还在从单张压缩图像重建动态 3D 场景方面超越了当前最先进的方法。