Perfecting Depth is a two-stage pipeline that uses diffusion-sample variance to flag unreliable depth pixels and a deterministic network to refine them, beating monocular baselines on indoor depth inpainting and noisy depth completion.
Normalizing flow ensem- bles for rich aleatoric and epistemic uncertainty modeling
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Perfecting Depth: Uncertainty-Aware Enhancement of Metric Depth
Perfecting Depth is a two-stage pipeline that uses diffusion-sample variance to flag unreliable depth pixels and a deterministic network to refine them, beating monocular baselines on indoor depth inpainting and noisy depth completion.