The depth supervision approach used in Simple-RF, while effective in providing guidance for learning depth in sparse input scenarios, may have some limitations that could be further improved.
One potential limitation is the reliance on reprojecting patches to determine the reliability of depth estimates. While this method is effective in certain cases, it may not capture all nuances of scene geometry and appearance, especially in complex scenes with intricate details. Improvements could be made by incorporating additional metrics or criteria for assessing the accuracy of depth estimates, such as geometric consistency or semantic understanding.
Furthermore, the threshold 𝑒𝜏 used to filter out unreliable depth estimates may impact the supervision process. Fine-tuning this threshold and exploring adaptive or dynamic thresholding mechanisms based on scene characteristics could enhance the robustness and accuracy of depth supervision.
To further improve the depth supervision approach, integrating multi-view consistency constraints, leveraging semantic information, or incorporating uncertainty estimation techniques could enhance the reliability and quality of depth estimates, leading to more accurate and consistent reconstructions in sparse input scenarios.