Pixel-wise Planarity for High-Precision Monocular Plane Segmentation

Plane segmentation from a single RGB image remains chal-lenging due to imprecise region grouping and geometrically inconsistentsupervision, often leading to over-segmentation and false planar detections.We propose instead a pixel-wise planarity prediction framework for ro-bust monocular plane segmentation. Building on a pretrained monoculargeometric backbone predicting depth and surface normals, we introducea dedicated planarity head that estimates per-pixel planarity confidence.During inference, predicted depth, normals, and planarity are combinedin a lightweight region-growing procedure that enforces geometric consis-tency when forming plane segments. We further analyze existing planeground-truth annotations and demonstrate substantial geometric incon-sistencies under strict distance thresholds. Across multiple datasets, ourmethod achieves improved geometric precision and segmentation qualitycompared to prior state-of-the-art approaches, while improving computa-tional efficiency.