DGNS：用于单目动态3D重建的可变形高斯泼溅与动态神经表面
DGNS: Deformable Gaussian Splatting and Dynamic Neural Surface for Monocular Dynamic 3D Reconstruction
AI总结：
本文提出DGNS混合框架，结合可变形高斯泼溅与动态神经表面，通过深度图引导光线采样与几何监督，实现单目动态3D重建与新视角合成的最先进性能。
摘要：
从单目视频重建动态场景对现实应用至关重要。我们提出了DGNS，这是一个集成了可变形高斯泼溅（Deformable Gaussian Splatting）与动态神经表面（Dynamic Neural Surfaces）的混合框架，同时有效解决了动态新视角合成与3D几何重建问题。训练期间，可变形高斯泼溅模块生成的深度图引导光线采样以加快处理速度，并在动态神经表面模块内提供深度监督以改善几何重建。反之，动态神经表面指导高斯基元在表面周围的分布，从而提升渲染质量。此外，我们提出了一种深度过滤方法以进一步细化深度监督。在公开数据集上的大量实验表明，DGNS在3D重建上达到了最先进的性能，并在新视角合成上取得了有竞争力的结果。
英文摘要：
Dynamic scene reconstruction from monocular video is essential for real-world applications. We introduce DGNS, a hybrid framework integrating \underline{D}eformable \underline{G}aussian Splatting and Dynamic \underline{N}eural \underline{S}urfaces, effectively addressing dynamic novel-view synthesis and 3D geometry reconstruction simultaneously. During training, depth maps generated by the deformable Gaussian splatting module guide the ray sampling for faster processing and provide depth supervision within the dynamic neural surface module to improve geometry reconstruction. Conversely, the dynamic neural surface directs the distribution of Gaussian primitives around the surface, enhancing rendering quality. In addition, we propose a depth-filtering approach to further refine depth supervision. Extensive experiments conducted on public datasets demonstrate that DGNS achieves state-of-the-art performance in 3D reconstruction, along with competitive results in novel-view synthesis.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
