Previous surface reconstruction methods either suffer from low geometric accuracy or lengthy training times when dealing with real-world complex dynamic scenes involving multi-person activities, and human-object interactions. To tackle the dynamic contents and the occlusions in complex scenes, we present a space-time 2D Gaussian Splatting approach. Specifically, to improve geometric quality in dynamic scenes, we learn canonical 2D Gaussian splats and deform these 2D Gaussian splats while enforcing the disks of the Gaussian located on the surface of the objects by introducing depth and normal regularizers. Further, to tackle the occlusion issues in complex scenes, we introduce a compositional opacity deformation strategy, which further reduces the surface recovery of those occluded areas. Experiments on real-world sparse-view video datasets and monocular dynamic datasets demonstrate that our reconstructions outperform state-of-the-art methods, especially for the surface of the details. The project page and more visualizations can be found at: this https URL.
以往的表面重建方法在处理涉及多人活动和人-物交互的复杂动态场景时，要么几何精度较低，要么训练时间较长。为了解决复杂场景中的动态内容和遮挡问题，我们提出了一种时空二维高斯分布（2D Gaussian Splatting）方法。具体而言，为了提升动态场景中的几何质量，我们学习了标准的二维高斯点，并对这些二维高斯点进行变形，同时通过引入深度和法线正则化，使高斯的圆盘位于物体表面上。此外，为了应对复杂场景中的遮挡问题，我们提出了一种组合的不透明度变形策略，进一步减少了遮挡区域的表面恢复问题。在稀疏视角视频数据集和单目动态数据集上的实验表明，我们的重建结果在表面细节方面优于现有最先进的方法。