GSFusion：当高斯泼浆与TSDF融合相遇时的在线RGB-D建图
GSFusion: Online RGB-D Mapping Where Gaussian Splatting Meets TSDF Fusion
AI总结：
提出GSFusion，将3D高斯泼浆融入体积建图系统，利用四叉树减少初始化泼浆数量，同时生成紧凑高斯地图和体积地图，提升计算效率且保持渲染质量。
摘要：
传统的体积融合算法保留了3D场景的空间结构，这对计算机视觉和机器人学中的许多任务是有益的。然而，它们在可视化方面往往缺乏真实感。新兴的3D高斯泼浆弥合了这一差距，但现有的基于高斯的重建方法常常出现伪影，且与底层3D结构不一致，并在实时优化方面遇到困难，无法为用户提供高质量即时反馈。瓶颈之一源于优化过程中需要更新的大量高斯参数。我们不将3D高斯作为独立的地图表示，而是将其纳入体积建图系统以利用几何信息，并提议在图像上使用四叉树数据结构来大幅减少初始化的泼浆数量。这样，我们同时生成了具有更少伪影的紧凑3D高斯地图和体积地图。我们的方法GSFusion显著提升了计算效率而不牺牲渲染质量，这在合成和真实数据集上得到了验证。代码将在https://github.com/goldoak/GSFusion提供。
英文摘要：
Traditional volumetric fusion algorithms preserve the spatial structure of 3D scenes, which is beneficial for many tasks in computer vision and robotics. However, they often lack realism in terms of visualization. Emerging 3D Gaussian splatting bridges this gap, but existing Gaussian-based reconstruction methods often suffer from artifacts and inconsistencies with the underlying 3D structure, and struggle with real-time optimization, unable to provide users with immediate feedback in high quality. One of the bottlenecks arises from the massive amount of Gaussian parameters that need to be updated during optimization. Instead of using 3D Gaussian as a standalone map representation, we incorporate it into a volumetric mapping system to take advantage of geometric information and propose to use a quadtree data structure on images to drastically reduce the number of splats initialized. In this way, we simultaneously generate a compact 3D Gaussian map with fewer artifacts and a volumetric map on the fly. Our method, GSFusion, significantly enhances computational efficiency without sacrificing rendering quality, as demonstrated on both synthetic and real datasets. Code will be available at https://github.com/goldoak/GSFusion.
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。
