The current Multi-View Stereo (MVS) depth estimation network, while effective, has several potential limitations. One significant issue is its reliance on keyframe selection, which may not always capture sufficient geometric information, especially in dynamic or complex environments. This can lead to inaccuracies in the estimated depth maps, affecting the quality of the Gaussian mapping. Additionally, the network's performance may degrade in low-texture areas or under varying lighting conditions, where depth estimation becomes challenging.
To improve the accuracy and robustness of the MVS depth estimation network, several enhancements can be considered. First, incorporating a more sophisticated feature extraction mechanism, such as attention-based models, could help the network focus on relevant features across different views, improving depth estimation in challenging scenarios. Second, integrating temporal information from consecutive frames could enhance depth consistency over time, allowing the network to leverage motion cues for better depth predictions. Finally, training the MVS network on a more diverse dataset that includes various environments and conditions could help it generalize better, leading to improved performance in real-world applications.