Metric Depth Estimation from Arbitrarily Degraded Low-Resolution Depth Prompts

Kun Wang; Yun Zhu; Pan Zhou; Na Zhao

cs.CVFeb 10, 2026

Metric Depth Estimation from Arbitrarily Degraded Low-Resolution Depth Prompts

Authors: Kun Wang, Yun Zhu, Pan Zhou, Na Zhao

Organizations: IMPL, Singapore University of Technology and Design · PCA Lab, Nanjing University of Science and Technology · LV-Lab, Singapore Management University

Abstract

We propose AdaDS, an Adaptive Diffusion Sampling framework for prompted metric depth estimation, which estimates high-resolution metric depth from an image and an arbitrarily degraded low-resolution depth prompt. This setting is commonly studied as depth super-resolution, where existing methods typically regress depth directly without an explicit prior over high-quality depth, and thus often produce artifacts under severe or unseen degradations. In contrast, AdaDS exploits the contraction property of Gaussian smoothing: as noise accumulates in the forward diffusion process, the noisy distributions of degraded prompts and their high-quality counterparts converge toward an isotropic Gaussian prior, while the prompt content is gradually erased. Accordingly, AdaDS refines the prompt and estimates its uncertainty to adaptively select a starting timestep, and injects learned noise to place the intermediate sample in a high-density region of the depth distribution learned by a pre-trained diffusion model. Denoising from this sample yields high-quality depth with fine-grained details and corrects residual errors, even under imperfect prompt refinement. Experiments on real-world and synthetic benchmarks demonstrate its superior zero-shot generalization and robustness to diverse degradations.

Figures & tables

Figure 1: Given an RGB image and an arbitrarily degraded low-resolution depth prompt, AdaDS adaptively places the intermediate noisy sample in a high-density region of a pre-trained depth diffusion prior, enabling zero-shot estimation of fine-grained high-resolution metric depth.

Figure 2: The distribution of the degraded depth prompt (with predicted mean and variance from coarse refinement) progressively aligns with the ground-truth distribution due to Gaussian smoothing contraction. Dots indicate the means.

Figure 3: Overall framework of AdaDS. (a) The first stage produces a refined depth latent along with an estimated uncertainty map reflecting the refinement reliability. (b) The second stage blends the coarse refinement with estimated noise, placing it in a high-density region of the high-quality depth distribution. The resulting intermediate noisy latent is denoised by a pre-trained relative depth diffusion model to recover the final depth map. $\mathcal{E}_{p}$ , $\mathcal{E}_{l}$ and $\mathcal{D}_{l}$ denote the patch embedding, latent encoder and latent decoder, respectively.

Figure 4: As $t$ increases, the reconstructed depth maps exhibit sharper structural details but deviate further from the input depth prompt, so that the error first decreases and then increases. All depth maps share a unified color palette, with RMSE reported in centimeters.

Method	Ref.	RGB-D-D						TOFDSR
		$192\times 144$		$64\times 48$		$32\times 24$		$192\times 144$		$64\times 48$		$32\times 24$
		RMSE $\downarrow$	MAE $\downarrow$	RMSE $\downarrow$	MAE $\downarrow$	RMSE $\downarrow$	MAE $\downarrow$	RMSE $\downarrow$	MAE $\downarrow$	RMSE $\downarrow$	MAE $\downarrow$	RMSE $\downarrow$	MAE $\downarrow$
DA v2-L	NeurIPS-24	0.1082	0.0745	0.1082	0.0745	0.1082	0.0745	0.1035	0.0690	0.1035	0.0690	0.1035	0.0690
MG-LCM	CVPR-24	0.1306	0.1007	0.1306	0.1007	0.1306	0.1007	0.1219	0.0835	0.1219	0.0835	0.1219	0.0835
DA v3-NGL	ICLR-26	0.2835	0.2528	0.2835	0.2528	0.2835	0.2528	0.5229	0.3806	0.5229	0.3806	0.5229	0.3806
SGNet	AAAI-24	0.0726	0.0388	0.0723	0.0391	0.0740	0.0416	0.0726	0.0400	0.0726	0.0404	0.0767	0.0433

Table 1: Quantitative zero-shot comparison on real-world benchmarks. Input resolutions for the low-resolution depth prompt are provided in the second row. For each metric, the best and second-best results are highlighted in bold and underline , respectively.

Method	Ref.	ScanNet					NYUv2
Method	Ref.	$16\times$ DS.	DS. + GN.	DS. + GB.	DS. + SM.	DS. + LC.	$16\times$ DS.	DS. + GN.	DS. + GB.	DS. + SM.	DS. + LC.
DA v2-L	NeurIPS-24	0.1416	0.1416	0.1416	0.1416	0.1416	0.2075	0.2075	0.2075	0.2075	0.2075
MG-LCM	CVPR-24	0.1986	0.1986	0.1986	0.1986	0.1986	0.2587	0.2587	0.2587	0.2587	0.2587
DA v3-NGL	ICLR-26	0.2186	0.2186	0.2186	0.2186	0.2186	0.3095	0.3095	0.3095	0.3095	0.3095
PromptDA	CVPR-25	0 .0927	0.0983	0 .0935	0 .0953	0.0950	0 .1326	0 .1352	0 .1349	0 .1369	0 .1338
OMNI-DC	ICCV-25	0.1173	0.1224	0.1127	0.1188	0.1206	0.1966	0.2011	0.1894	0.2003	0.1980

Table 2: Quantitative zero-shot comparison on synthetic benchmarks. DS., GN., GB., SM. and LC. denote downsampling, Gaussian noise, Gaussian blur, sparse measurement and low-bit compression, respectively. RMSE metric is reported in this table.

Figure 5: Comparison on real-world benchmarks with arbitrary upsampling factors. The

\delta_{1.05}

\uparrow

metric is reported in this figure.

Figure 6: Qualitative comparison on real-world benchmarks. The first two samples are from RGB-D-D and the last two from TOFDSR. Blue boxes mark regions enlarged in the bottom-left inset of each result, and error maps are shown in the bottom-right inset, with warmer colors denoting larger errors.

Figure 7: Qualitative comparison on synthetic benchmarks. The first two samples are from NYUv2 and the last two from ScanNet. To facilitate 3D structural evaluation, the input and estimated depth maps are back-projected into point clouds and colorized with the corresponding RGB images.

Table 12

Depth Estimation

Depth estimation infers scene distance from images and sensors, enabling 3D reconstruction, navigation, and spatial understanding.

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Abstract

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