Gaussian-SLAM vs SplaTAM

Gaussian-SLAM excels at large-scale, consistent mapping because it partitions the environment into discrete sub-maps. This strategy effectively contains catastrophic forgetting and drift, enabling robust performance in extensive, multi-room environments. For example, on the TUM-RGBD benchmark, Gaussian-SLAM demonstrates superior trajectory accuracy over long sequences by isolating optimization to active sub-maps, preventing global map corruption.

SplaTAM takes a different approach by maintaining a single, explicit 3D Gaussian map for the entire scene. This results in a simpler system architecture with no sub-map stitching logic, allowing for immediate, holistic scene updates. The key trade-off is that while SplaTAM achieves higher-fidelity novel view synthesis in bounded scenes, its single-map representation can suffer from catastrophic forgetting and increased computational cost as the explored area expands.

The key trade-off: If your priority is robust, long-term mapping accuracy in large, multi-room environments, choose Gaussian-SLAM for its sub-map management. If you prioritize maximum photorealistic rendering quality in smaller, bounded spaces with a simpler codebase, choose SplaTAM.

[Gaussian-SLAM] excels at scalability and map consistency because it decomposes the environment into discrete, manageable sub-maps. This architecture prevents catastrophic forgetting and memory bloat in large-scale environments, making it the superior choice for extensive floor plans or multi-room sequences. In benchmarks, this sub-map approach demonstrates more robust long-term tracking stability, as local optimizations do not degrade the global map quality.

[SplaTAM] takes a fundamentally different approach by maintaining a single, unified explicit map represented entirely by 3D Gaussians. This results in a simpler system architecture with fewer hyperparameters to tune. The key trade-off is that it achieves higher-fidelity dense reconstruction and more photorealistic novel view synthesis in bounded scenes, as the unified representation allows for seamless hole-filling and consistent global illumination without boundary artifacts.

The key trade-off: If your priority is robustness in large, multi-room environments and long-term autonomy, choose Gaussian-SLAM for its superior scalability and drift management. If you prioritize maximum visual fidelity and dense reconstruction quality in a confined space, such as a single workcell or room, choose SplaTAM for its seamless, high-quality map representation.