We propose DiffusionDepth, a new approach that reformulates monocular depth estimation as a denoising diffusion process.
Feb 28, 2023 · We formulate monocular depth estimation using denoising diffusion models, inspired by their recent successes in high fidelity image generation.
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Abstract. We formulate monocular depth estimation using denoising diffusion models, inspired by their recent successes in high fidelity image generation.
Dec 4, 2023 · We introduce Marigold, a method for affine-invariant monocular depth estimation that is derived from Stable Diffusion and retains its rich prior knowledge.
We present DMD (Diffusion for Metric Depth), a state-of-the-art diffusion model for monocular absolute depth estimation.
We present a method to estimate dense depth by optimizing a sparse set of points such that their diffusion into a depth map minimizes a multi-view reprojection ...
We introduce a novel self-supervised depth estimation framework, dubbed MonoDiffusion, by formulating it as an iterative denoising process.
Figure 1. We present Marigold, a diffusion model and associated fine-tuning protocol for monocular depth estimation. Its core.
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