Objective Existing RGBD semantic segmentation algorithms fail to fully utilize shape information provided by depth images and suffer from high computational costs particularly for complex lighting diverse materials and spatial structures in indoor scenes. This paper proposed an efficient semantic segmentation method for indoor RGBD scenes based on SwiftNet. Methods Firstly in the SwiftNet a lightweight multi-scale road RGB scene semantic segmentation algorithm depth images were incorporated. By leveraging the color stability of depth images and the distance information provided for each pixel relative to the camera this approach reduced the impact of factors such as lighting color variations and distances on segmentation results. Next a specialized extraction of geometric shape features from depth images was conducted. Depth features were decomposed into positional components and shape components with two learnable weights introduced to independently collaborate with them. Convolution operations were then applied for the reweighting and combination of these two components securing the intrinsic geometric shape information from the depth data without incurring additional computation and memory during the inference phase. Finally to capture richer contextual information more rapidly the depth aggregation pyramid pooling module was enhanced to extract context information in parallel referred to as the Fast Aggregation Pyramid Pooling Module FAPPM . Results Through evaluation experiments on the NYUv2 and SUNRGBD indoor datasets the results demonstrated that compared with the current well-performing ESANet model the proposed approach achieved improvements of 2. 21% and 3. 2% in mean intersection over union MIoU on these datasets respectively. Furthermore it achieves a processing speed of 33. 36 frames per second FPS . Conclusion The validation confirms the algorithm?? s efficiency and accuracy in handling complex indoor semantic segmentation tasks providing solid support for subsequent intelligent robot tasks in indoor applications.