The guided depth diffusion approach, while effective in mitigating geometric distortion, has several potential limitations. One significant challenge is the reliance on the quality of the initial depth estimates. If the estimated depth from the monocular input image is inaccurate, it can lead to compounded errors in the final generated scene, resulting in misalignments and inconsistencies.
To improve the guided depth diffusion method, one could implement a feedback loop that continuously refines depth estimates based on the evolving geometry of the scene. This could involve using a more sophisticated depth estimation model that incorporates temporal coherence, allowing the system to learn from previous iterations and adjust depth predictions accordingly.
Moreover, integrating additional contextual information, such as semantic segmentation or object recognition, could enhance the depth guidance process. By understanding the scene's content better, the system could apply more targeted corrections to the depth map, ensuring that the generated geometry aligns more closely with the intended scene structure.
Lastly, exploring hybrid models that combine depth diffusion with other generative techniques, such as neural radiance fields or mesh-based representations, could provide a more comprehensive solution for achieving tighter geometric consistency across generated scenes.