AI 中文总结
针对现有多房间布局估计方法泛化差、未利用建筑结构的问题，提出 PolyLayout 方法，通过联合优化跨房间的曼哈顿 3D 多边形，在新基准上实现了优于现有方法的准确性与鲁棒性。
AI 中文摘要
从多视图图像估计房间布局是室内场景理解的核心任务。现有方法通常受限于对新数据集的泛化能力差，或对房间形状、相机配置的几何假设过于严格；大多数方法还会独立估计各个房间，无法利用主导方向、地面平面或天花板高度等共享建筑结构。我们提出 PolyLayout，这是一种多房间布局估计方法，将房间布局参数化为曼哈顿 3D 多边形，并跨多个房间联合优化它们。优化目标由在鲁棒预训练视觉特征之上的神经网络预测，仅在输出房间布局的监督下端到端训练；同时，相机投影和多边形更新保持显式且基于模型。这种学习评分与几何之间的分离提高了对新数据集和相机参数的泛化能力。优化期间，PolyLayout 通过迭代的墙拆分和合并操作自适应地优化多边形拓扑，同时联合利用跨房间的结构线索。我们通过为现有数据集提供布局注释引入了两个新的多视图多房间布局基准，实验表明，PolyLayout 在准确性和鲁棒性方面均优于现有方法。项目页面：this https URL
英文摘要
Estimating room layouts from multi-view imagery is a core task for indoor scene understanding. Existing methods are typically limited either by poor generalization to new datasets or restrictive geometric assumptions of the room shape or camera configuration. Most also estimate rooms independently, failing to exploit shared building structure such as dominant directions, ground plane or ceiling height. We propose PolyLayout, a multi-room layout estimation method that parameterizes room layouts as Manhattan 3D polygons and optimizes them jointly across multiple rooms. The optimization objective is predicted by a neural network on top of robust pre-trained visual features and trained end-to-end with supervision only on output room layouts. At the same time, camera projection and polygon updates remain explicit and model-based. This separation between learned scoring and geometry improves generalization to new datasets and camera parameters. During optimization, PolyLayout adaptively refines the polygon topology through iterative wall split and merge operations while jointly utilizing structural cues across rooms. We introduce two new multi-view multi-room layout benchmarks by providing layout annotations to existing datasets, and experiments show that PolyLayout outperforms prior approaches, both in terms of accuracy and robustness. Project page: https://ghanning.github.io/PolyLayout
CommentsAccepted at the European Conference on Computer Vision (ECCV) 2026
DOI：10.1007/978-3-032-37023-5_18
