In SPIdepth, the pose network plays a crucial role in estimating the relative pose between input and reference images for view synthesis. To further improve the pose network's ability to capture intricate scene structures and geometric relationships, several enhancements can be considered:
Multi-scale Feature Fusion: Integrate multi-scale feature fusion techniques to capture details at different levels of abstraction, allowing the network to understand scene structures comprehensively.
Attention Mechanisms: Incorporate attention mechanisms to focus on relevant parts of the image, enabling the network to prioritize important regions for depth estimation.
Graph Neural Networks: Utilize graph neural networks to model spatial dependencies within the scene, enhancing the network's understanding of complex geometric relationships.
Adaptive Sampling Strategies: Implement adaptive sampling strategies to gather more information from regions with high uncertainty, improving the network's accuracy in estimating depth in challenging areas.
Consistency Constraints: Introduce additional consistency constraints between predicted depth maps and image features to ensure the network maintains coherence in its estimations across different frames.