To further improve the SAGS method for more accurate depth estimation and scene understanding, several enhancements can be considered:
Multi-Resolution Encoding: Introducing a multi-resolution encoding scheme can capture fine details and depth variations in the scene, enhancing the accuracy of depth estimation.
Attention Mechanisms: Integrating attention mechanisms into the graph neural network architecture can help focus on relevant scene features and improve depth estimation in complex scenes.
Adaptive Sampling: Implementing adaptive sampling techniques can ensure that critical areas of the scene with high depth variations are adequately represented, leading to more precise depth maps.
Dynamic Graph Construction: Developing a dynamic graph construction approach that adapts to the scene's complexity and structure can optimize the encoding process and enhance scene understanding.
Feedback Mechanisms: Incorporating feedback mechanisms to refine depth estimates based on rendered images can iteratively improve the accuracy of depth maps and scene representation.
By incorporating these enhancements, the SAGS method can achieve more accurate depth estimation and scene understanding, making it more suitable for VR/AR applications requiring precise spatial information.