发表机构
ETH Zurich; Microsoft Spatial AI Lab（苏黎世联邦理工学院; 微软空间智能实验室）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出像素级平面性预测框架，结合深度与法线，通过区域生长实现高精度单目平面分割，提升几何精度与效率。
AI 中文摘要
从单张RGB图像进行平面分割仍然具有挑战性，原因在于区域分组不精确和几何不一致的监督，常常导致过度分割和虚假的平面检测。我们提出了一种像素级平面性预测框架，用于鲁棒的单目平面分割。基于一个预训练的单目几何骨干网络（预测深度和表面法线），我们引入了一个专门的平面性头，用于估计每个像素的平面性置信度。在推理过程中，预测的深度、法线和平面性在一个轻量级的区域生长过程中结合，该过程在形成平面段时强制执行几何一致性。我们进一步分析了现有的平面地面真值标注，并证明了在严格的距离阈值下存在显著的几何不一致性。在多个数据集上，我们的方法相比先前的最先进方法实现了更高的几何精度和分割质量，同时提高了计算效率。我们的代码和模型可在以下网址获取：此https URL。
英文摘要
Plane segmentation from a single RGB image remains challenging due to imprecise region grouping and geometrically inconsistent supervision, often leading to over-segmentation and false planar detections. We propose instead a pixel-wise planarity prediction framework for robust monocular plane segmentation. Building on a pretrained monocular geometric backbone predicting depth and surface normals, we introduce a dedicated planarity head that estimates per-pixel planarity confidence. During inference, predicted depth, normals, and planarity are combined in a lightweight region-growing procedure that enforces geometric consistency when forming plane segments. We further analyze existing plane ground-truth annotations and demonstrate substantial geometric inconsistencies under strict distance thresholds. Across multiple datasets, our method achieves improved geometric precision and segmentation quality compared to prior state-of-the-art approaches, while improving computational efficiency. Our code and models are available at https://github.com/alpayozkan/PixelwisePlanarity.
CommentsTo appear at ECCV 2026. Code available at https://github.com/alpayozkan/PixelwisePlanarity
