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.

翻译：传统体素融合算法保留了三维场景的空间结构，这对计算机视觉和机器人领域的诸多任务具有重要价值。然而，这类方法在可视化效果上往往缺乏真实感。新兴的三维高斯溅射技术填补了这一空白，但现有基于高斯的重建方法常存在伪影、与底层三维结构不一致的问题，且难以实现实时优化，无法为用户提供高质量即时反馈。其中一个瓶颈源于优化过程中需要更新的海量高斯参数。我们并未将三维高斯作为独立的建图表征，而是将其整合至体素建图系统中以利用几何信息，并提出在图像上采用四叉树数据结构以大幅减少初始化的溅射点数量。通过这种方式，我们能够同步生成紧凑且伪影更少的三维高斯地图与实时体素地图。我们的方法GSFusion在合成与真实数据集上的实验表明，其在保持渲染质量的同时显著提升了计算效率。代码将在https://github.com/goldoak/GSFusion开源。