The proposed depth smooth loss and Sparse-Dense Adjustment Ring (SDAR) strategy can be effectively extended to other differentiable rendering-based SLAM systems by integrating these techniques into their existing frameworks. The depth smooth loss, which minimizes discrepancies between adjacent pixel depth values, can enhance geometric reconstruction by ensuring that depth maps generated from rendered images maintain spatial coherence. This can be particularly beneficial in systems that rely on depth maps from RGB-D sensors or other depth estimation methods, where noise and inaccuracies are common. By applying a similar smoothness constraint, these systems can reduce artifacts and improve the overall quality of the reconstructed scene.
Moreover, the SDAR strategy, which aligns the scale between sparse visual odometry and dense Gaussian maps, can be adapted to other SLAM systems by implementing a similar mechanism for scale correction. This could involve using a statistical approach to align depth estimates from different sources, ensuring that the scale of the reconstructed scene remains consistent across various frames. By incorporating these strategies, differentiable rendering-based SLAM systems can achieve improved geometric accuracy and robustness, ultimately leading to better performance in real-world applications.