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Semantic segmentation of RGB-D images involves understanding the appearanceand spatial relationships of objects within a scene, which requires carefulconsideration of various factors. However, in indoor environments, the simpleinput of RGB and depth images often results in a relatively limited acquisitionof semantic and spatial information, leading to suboptimal segmentationoutcomes. To address this, we propose the Multi-modal Interaction and PoolingAttention Network (MIPANet), a novel approach designed to harness theinteractive synergy between RGB and depth modalities, optimizing theutilization of complementary information. Specifically, we incorporate aMulti-modal Interaction Fusion Module (MIM) into the deepest layers of thenetwork. This module is engineered to facilitate the fusion of RGB and depthinformation, allowing for mutual enhancement and correction. Additionally, weintroduce a Pooling Attention Module (PAM) at various stages of the encoder.This module serves to amplify the features extracted by the network andintegrates the module's output into the decoder in a targeted manner,significantly improving semantic segmentation performance. Our experimentalresults demonstrate that MIPANet outperforms existing methods on two indoorscene datasets, NYUDv2 and SUN-RGBD, underscoring its effectiveness inenhancing RGB-D semantic segmentation.

Optimizing rgb-d semantic segmentation through multi-modal interaction and pooling attention