Distilling Monocular Foundation Model for Fine-grained Depth Completion

Figure 1 . Depth completion models trained solely with L1 loss and sparse ground truth produce incomplete and fragmented depth predictions. Our framework, however, demonstrates significant improvements by distilling knowledge from monocular foundation models and incorporating a scale-and shift-invariant loss (SSI Loss), resulting in more complete and accurate dense depth completion.