# Sem-SLAM: Semantic-Integrated SLAM Approach for 3D Reconstruction
## 2 Related Work
Heavy reliance on synthetic data in numerous studies exacerbates the domain gap when models encounter real-world complexity, exposing the fragility of system robustness. **Semantic SLAM**  
The integration of Semantic Semantic SLAM and NeRF aims to enhance the quality of scene understanding and reconstruction by fusing semantic information. In the context of SLAM in dynamic environments, which confront challenges such as tracking drift and mapping errors, DDN-SLAM [16] has pioneered the introduction of semantic features, giving rise to the first real-time dense dynamic neural implicit SLAM system. By integrating semantic information, this system effectively addresses the challenges in complex dynamic scenarios, significantly improving the accuracy and stability of localization and mapping. It thus sets a new milestone for the development of SLAM technology. Concurrently, the SNI-SLAM [17] approach takes an alternative route and proposes a semantic SLAM framework based on neural implicit representation. This system not only achieves high-precision semantic mapping and high-quality surface reconstruction but also, through the introduction of a hierarchical semantic representation strategy, enables top-down structured semantic mapping. As a result, it ensures the robustness and accuracy of camera tracking. This innovation not only enriches the theoretical system of semantic SLAM but also provides a powerful technical support for practical applications. In the pursuit of efficient and accurate SLAM solutions, the EC-SLAM [18] system represents maps using sparse parameter encoding and Truncated Signed Distance Fields (TSDF), thereby achieving enhanced efficiency and accelerated convergence. This approach not only ensures map accuracy but also significantly reduces computational costs, rendering real-time applications of SLAM technology feasible. To address the deficiencies in the tracking performance of NeRF-SLAM systems, SLAIM [19] proposes a novel coarse-to-fine tracking model. By optimizing the tracking strategy, this model effectively improves the tracking performance of NeRF-SLAM, marking a new breakthrough in SLAM technology based on neural radiance fields. Furthermore, the iDF-SLAM [8] system combines a feature-based deep neural tracker with a NeRF-like neural implicit mapper, enabling a tight integration of front-end tracking and back-end mapping. This system can learn and utilize scene-specific features for camera tracking, thus maintaining stable localization and mapping capabilities in complex environments. There remain challenges such as how to more precisely interpret and fuse semantic information, as well as how to achieve high-quality environmental reconstruction. In response to these issues, the CG-SLAM [20] system presents an innovative approach based on uncertainty-aware 3D Gaussian fields. Through in-depth analysis of 3D Gaussian splitting techniques, this method constructs consistent and stable 3D Gaussian fields, providing a solid foundation for tracking and mapping. In conclusion, this paper proposes to combine the cross-modal full cross-attention mechanism with the multi-scale information fusion reconstruction paradigm. By deeply fusing semantic, shape, and geometric features, the performance of the neural implicit SLAM system is enhanced. This approach enables the system to capture a more complete set of environmental characteristics, thereby improving its robustness and accuracy in complex scenarios.