# Sem-SLAM: Semantic-Integrated SLAM Approach for 3D Reconstruction
## 2 Related Work  
**Implicit Neural Representation**  
The application of implicit neural representations in SLAM has emerged as a prominent research area in recent years. These methods leverage deep learning techniques to represent environmental map information. They are capable of providing high-fidelity dense maps and have demonstrated promising results in indoor environments [8; 9]. However, such approaches also confront several challenges, such as issues regarding robustness in complex or dynamic scenarios [10]. To address these problems, researchers have proposed a series of improvement methods to enhance the performance of implicit neural SLAM systems:To better handle RGB images without depth information, a design of hierarchical feature volumes has been proposed to effectively fuse shape cues at different scales [11]. This approach enables more comprehensive utilization of visual information, facilitating more accurate map construction. The DF-SLAM system represents scenes using dictionary factors. It encodes geometric and appearance information as a combination of basis and coefficient factors, thereby enhancing the ability to reconstruct details [12]. This innovative encoding method improves the precision of scene reconstruction, especially in capturing fine-grained features. NICE-SLAM [13] exploits local information to improve the reconstruction effect in large-scale scenes. By focusing on local details and integrating them into the overall reconstruction, it achieves better results in complex and extensive environments. The GO-SLAM [14] framework emphasizes the consistency of global optimization for pose estimation and 3D reconstruction. It supports efficient loop-closure detection and online full bundle adjustment, resolving the interference of dynamic environments on reconstruction. This framework effectively addresses the challenges posed by dynamic elements in the environment, ensuring more stable and accurate SLAM performance. Additionally, to enhance the system's robustness, a SLAM system based on event data has been proposed [15]. This system can maintain stability under complex conditions such as illumination changes and motion blur. The use of event-based data provides an alternative and more robust way to perceive the environment, expanding the applicability of SLAM systems in challenging scenarios. Notwithstanding the remarkable progress achieved, the existing neural implicit SLAM methods still have certain inadequacies. Currently, most methods primarily rely on color information within images, neglecting other crucial environmental features. This narrow focus limits their comprehensive understanding and accurate representation of the environment. Although NeRF-SLAM research in 2024-2025 appears to yield fruitful results, it still faces critical bottlenecks in core technologies, remaining far from practical application. In terms of computational efficiency, most methods require several seconds to process each frame even with high-end GPUs, making them impractical for real-time applications such as autonomous driving and augmented reality. The limited scene generalization capability of RGB-D-based algorithms is another major weakness--every transition between environments (e.g., indoor to outdoor, daylight to nighttime) demands model retraining, with prohibitive time and computational costs that hinder adaptation to dynamic real-world conditions. Hardware compatibility is equally problematic. The insatiable demand for computing power restricts these algorithms to large-scale servers, rendering deployment on edge devices virtually impossible and severely limiting their applicability. Dynamic scene handling remains a \"Waterloo\" for such methods: rapidly moving objects or sudden environmental changes can instantly \"blind\" the system, generating extensive visual artifacts that directly compromise localization and mapping accuracy. The scientific rigor of evaluation systems is also questionable.