Graph-GSReg: 利用3D场景图进行高斯溅射配准
Graph-GSReg: Leveraging 3D Scene Graphs for Gaussian Splatting Registration
AI总结：
提出Graph-GSReg方法，通过构建3D场景图将3DGS配准转化为图配准问题，并引入自监督测试时优化以消除合并伪影，实现高精度配准和高质量场景融合。
摘要：
将多个3D高斯溅射（3DGS）场景合并为统一的单一高斯表示对于大规模3D映射和长期地图管理至关重要。尽管其重要性，这一领域仍未被充分探索，现有解决方案存在若干局限性。基于学习的方法试图建立高斯原语之间的直接对应关系，并需要在大型3DGS数据集上进行训练。基于图像的优化方法严重依赖于通用基础模型的粗略初始化，并且通常需要昂贵的细化。我们提出了Graph-GSReg。我们的方法从3DGS及其渲染图像构建3D场景图，将3DGS配准重新表述为图配准问题。所提出的3D场景图以更高层次的表示表示每个3DGS，从而实现对语义信息和结构上下文的全局一致理解，以实现精确配准。为了进一步构建无缝的统一场景，我们引入了自监督测试时优化。简单合并两个3D高斯场景通常会遭受遮挡伪影，如空洞和漂浮物。为了缓解这一问题，我们细化合并后的高斯以保持原始场景与合并场景之间的视觉一致性。我们在真实和合成基准上评估了我们的方法，展示了具有竞争力的配准精度和合并场景渲染质量。
英文摘要：
Merging multiple 3D Gaussian Splatting (3DGS) scenes into a single unified Gaussian representation is essential for large-scale 3D mapping and long-term map management. Despite its importance, this area remains underexplored, and existing solutions exhibit several limitations. Learning-based methods attempt direct correspondence between Gaussian primitives and require training on large 3DGS datasets. Image-based optimization methods depend heavily on coarse initialization from generic foundation models and often incur expensive refinement. We present \ourmodel. Our method constructs a 3D scene graph from a 3DGS and its rendered images, \textit{reformulating 3DGS registration as a graph registration problem}. The proposed 3D scene graph represents each 3DGS at a higher-level representation, enabling a globally consistent understanding of semantic information and structural context for accurate registration. To further construct a seamless unified scene, we introduce a Self-Supervised Test-Time Optimization. Naively merging two 3D Gaussian scenes often suffers from occlusion artifacts such as hollows and floaters. To alleviate this issue, we refine the merged Gaussians to preserve visual consistency between the original scenes and the merged scene. We evaluate our method on real and synthetic benchmarks, demonstrating competitive registration accuracy and merged scene rendering quality.
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。
