A Semantic Parsing Method for Indoor Scene Images Based on Prior Knowledge of Building Structure.

To address semantic prediction discontinuities and physical boundary distortions caused by furniture occlusion in complex indoor scenes, this paper proposes a semantic parsing method that leverages building-structure priors. The scheme uses a shifted-window hierarchical transformer encoder to extract multi-scale visual features and combines a gradient-direction-consistency line segment detection algorithm to construct a Manhattan 3D bounding box. This bounding box is transformed into a signed distance field (SDF) prior to encoding discrete geometric contours into a continuous physical potential field. A structure-guided cross-attention mechanism forces the visual signals to align with real 3D orthogonal geometric boundaries, restoring feature continuity in occluded areas. A spatial adjacency graph constructed from superpixel nodes drives a Graph Convolutional Network (GCN) to aggregate features, ensuring macroscopic semantic consistency within the physical load-bearing plane. A combination of pixel-level cross-entropy loss and a custom-designed structural consistency loss strengthens the constraints, penalizing out-of-bounds predictions. Experiments across multiple independent runs show that the mean intersection over union (mIoU) reaches 68.7% with a standard deviation of 0.2%, and the structural boundary F1 score reaches 76.4% with a standard deviation of 0.3%, confirming the robust performance of the proposed modules. With a single image node size of 256, the average inference time remained at 61 ms.