发表机构
Shenzhen University; Autodesk Research（深圳大学; 欧特克研究院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究旨在从原始图像提取结构化3D表示，提出TopoGS框架，通过提取多视图图像的2D拓扑关系，将高斯基元锚定到结构元素，联合优化平面参数等，在ScanNet++数据集上达最优性能，生成高质量3D模型。
AI 中文摘要
从原始图像中提取结构化、参数化的3D表示仍是计算机视觉和图形学的基本挑战。虽然3D高斯点云（3DGS）流程的最新进展集成平面基元以生成紧凑且可编辑的几何图形，但这些方法通常将平面视为孤立、离散的集合。缺乏拓扑连通性阻碍了强大的几何推理，导致重建碎片化和边界错位。为解决此问题，我们引入TopoGS，首个明确集成平面和拓扑约束以进行连贯3D重建的3DGS框架。具体而言，我们从多视图图像分割中提取全局2D拓扑关系，并将高斯基元锚定到这些结构元素。这种公式化使得能够联合优化平面参数、渲染保真度和拓扑邻接性。通过在这些拓扑约束下强制严格的多视图一致性，我们的方法显著减轻几何错位并生成连接的、结构化的3D模型。在ScanNet++数据集上的广泛评估表明，TopoGS实现了当前最优性能，为生成准确、拓扑合理且视觉逼真的场景表示提供了高度鲁棒的解决方案。
英文摘要
Extracting structured, parametric 3D representations from raw images remains a fundamental challenge in computer vision and graphics. While recent advancements in the 3D Gaussian Splatting (3DGS) pipeline integrate planar primitives to yield compact and editable geometry, these approaches typically treat planes as isolated, discrete sets. This lack of topological connectivity hinders robust geometric reasoning, leading to fragmented reconstructions and misaligned boundaries that fall short of the precision for rigorous spatial analysis and professional design workflows. To address this, we introduce TopoGS, the first 3DGS framework to explicitly integrate both planar and topological constraints for coherent 3D reconstruction. Specifically, we extract global 2D topological relationships from multi-view image segmentations and anchor Gaussian primitives to these structural elements. This formulation enables the joint optimization of plane parameters, rendering fidelity, and topological adjacency. By enforcing strict multi-view consistency alongside these topological constraints, our method significantly mitigates geometric misalignments and produces connected, structured 3D models. Extensive evaluations on the ScanNet++ dataset demonstrate that TopoGS achieves state-of-the-art performance, providing a highly robust solution for generating accurate, topologically sound, and visually faithful scene representations.
CommentsEuropean Conference on Computer Vision (Proceedings of ECCV 2026); Project page: https://vcc.tech/research/2026/TopoGS
