To further enhance the selective densification and pruning strategies in EfficientGS, several improvements can be considered:
Dynamic Threshold Adjustment: Instead of using fixed thresholds for determining non-steady state Gaussians and dominant Gaussians, dynamic thresholds based on the scene complexity or gradient distribution could be implemented. This adaptive approach would ensure that the strategies are optimized for each specific scene, leading to more efficient densification and pruning.
Machine Learning Integration: Machine learning algorithms could be employed to learn the patterns of Gaussian redundancies and importance, allowing for more accurate and automated selection of Gaussians for densification and pruning. This could lead to a more intelligent and efficient process.
Hierarchical Densification: Implementing a hierarchical densification approach where densification is performed at different levels of detail based on the importance of the Gaussians could further optimize the process. This hierarchical strategy could prioritize densification in critical areas while reducing unnecessary processing in less significant regions.
Adaptive Pruning Criteria: Instead of solely relying on Gaussian weights for pruning, additional criteria such as geometric significance, color consistency, or spatial coherence could be integrated. By considering multiple factors, the pruning strategy can be more comprehensive and effective in reducing redundant Gaussians.