# Sem-SLAM: Semantic-Integrated SLAM Approach for 3D Reconstruction
## 4 Experiments
### Qualitative Evaluations  
As shown in Figure 2, we selected two rooms from the Replica dataset to conduct camera pose trajectory experiments. The trajectory represents the camera's movement path in the environment, which is a crucial indicator for evaluating the positioning accuracy of the SLAM system. We aim to evaluate the accuracy of the proposed method in obtaining the camera pose trajectory. In the experiment, the blue line represents the trajectory obtained by our Sem-SLAM, and the black line represents the ground truth trajectory. The results show that the trajectory obtained by our method highly coincides with the ground truth trajectory, which effectively verifies the effectiveness of introducing the multi-scale information extractor to extract both coarse-grained and fine-grained information of the camera pose. With the help of this innovative structure, relevant trajectory information can be accurately captured, making the trajectory estimation approach the true value. In addition, we carried out a multi-view semantic mapping experiment for the same room. As shown in Figure 3, throughout the mapping process, relying on the full-attention cross-modal feature fusion mechanism, our algorithm can efficiently integrate the geometric, shape, and semantic features extracted by the cross-attention and self-attention modules. By fully exploiting the complementary advantages of features at various levels, the algorithm is able to accurately capture the subtle structures and semantic attributes of objects in the scene. However, due to the limitations of the viewing angles, there are some perceptual blind spots in certain areas, making it difficult to achieve a complete recognition in one go. However, as the mapping process progresses, the algorithm can, based on the accumulated multi-view information, dynamically fill in the missing details through an iterative optimization strategy. In this way, it gradually improves the integrity and accuracy of the map, and ultimately generates a more detailed and accurate semantic map. Finally, we conducted a detailed comparative experiment on semantic modeling across two rooms, as illustrated in Figure 4. By juxtaposing the generated semantic images with their ground truth counterparts, we observed a striking resemblance between them. Our algorithm, empowered by the multi-scale information extractor and full-attention cross-modal feature fusion approach, excels not only in accurately reconstructing semantic information at a holistic level but also in meticulously capturing fine-grained details. Figure 2: Qualitative comparison on scene reconstruction of our Sem-SLAM and baseline. The ground truth images and details are rendered with ReplicaViewer software (SDK [[https://github.com/facebookresearch/Replica-Dataset/tree/main](https://github.com/facebookresearch/Replica-Dataset/tree/main)]([https://github.com/facebookresearch/Replica-Dataset/tree/main](https://github.com/facebookresearch/Replica-Dataset/tree/main)) (accessed on 10 July 2025)) [6]. These are two rooms selected from the Replica dataset to conduct camera pose trajectory experiments. The trajectory represents the camera’s movement path in the environment, which is a crucial indicator for evaluating the positioning accuracy of the SLAM system. In this figure, if only the blue predicted trajectory is visible, it indicates that there is no deviation between the trajectories, and the black part in the figure represents the trajectory deviation. The interpolation results presented in the third column vividly demonstrate that, within specific object modeling regions, the algorithm employs interpolation techniques to  
Figure 4: Qualitative comparison on scene reconstruction of our method. The ground truth images and details are rendered with ReplicaViewer software. We visualize 2 selected scenes of the Replica dataset [6]. Figure 3: A multi-view semantic mapping experiment for the same room. Tabhe red and green lines represent the trajectories of the cameras in operation.