Current monocular estimation of metric depth faces challenges due to differences in indoor and outdoor datasets, scale ambiguity in photos, and limited generalizability. A new study by Google Research and Google Deepmind introduces DMD, a diffusion model for zero-shot metric depth estimation, achieving state-of-the-art performance by addressing scale ambiguities and enhancing generalizability. DMD outperforms ZoeDepth on various datasets, achieving a 25% lower relative error for indoor datasets and a 33% lower error for outdoor datasets.
Google Researchers Unveil DMD: A Groundbreaking Diffusion Model for Enhanced Zero-Shot Metric Depth Estimation
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