A lightweight refiner with a coarse-to-fine denoising module, noise-based pretraining, and a scale-shift invariant gradient-matching loss achieves state-of-the-art high-resolution metric depth with up to 10x faster inference.
On the over-smoothing problem of cnn based disparity estimation
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PatchRefiner V2: Fast and Lightweight Real-Domain High-Resolution Metric Depth Estimation
A lightweight refiner with a coarse-to-fine denoising module, noise-based pretraining, and a scale-shift invariant gradient-matching loss achieves state-of-the-art high-resolution metric depth with up to 10x faster inference.