Exploring the capabilities of Neural Radiance Fields (NeRF) and Gaussian-based methods in the context of 3D scene reconstruction, this study contrasts these modern approaches with traditional Simultaneous Localization and Mapping (SLAM) systems. Utilizing datasets such as Replica and ScanNet, we assess performance based on tracking accuracy, mapping fidelity, and view synthesis. Findings reveal that NeRF excels in view synthesis, offering unique capabilities in generating new perspectives from existing data, albeit at slower processing speeds. Conversely, Gaussian-based methods provide rapid processing and significant expressiveness but lack comprehensive scene completion. Enhanced by global optimization and loop closure techniques, newer methods like NICE-SLAM and SplaTAM not only surpass older frameworks such as ORB-SLAM2 in terms of robustness but also demonstrate superior performance in dynamic and complex environments. This comparative analysis bridges theoretical research with practical implications, shedding light on future developments in robust 3D scene reconstruction across various real-world applications.

本研究探讨了神经辐射场（NeRF）与基于高斯的方法在3D场景重建中的能力，针对传统的同步定位与地图构建（SLAM）系统进行了对比。研究发现，尽管NeRF在视图合成上表现优异，但处理速度较慢，而高斯方法则提供快速处理但场景完善性不足，对3D场景重建的未来发展具有重要的实际意义。

评估现代3D场景重建方法：NeRF与基于高斯的方法