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<!DOCTYPE html>
<html>
<head>
<meta charset="utf-8">
<meta name="description"
content="SharpDepth: Sharpening Metric Depth Predictions Using Diffusion Distillation">
<meta name="keywords" content="depth, estimation, monocular, scene, reconstruction">
<meta name="viewport" content="width=device-width, initial-scale=1">
<title>SharpDepth: Sharpening Metric Depth Predictions Using Diffusion Distillation</title>
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<body>
<section class="hero">
<div class="hero-body">
<div class="container is-max-desktop">
<div class="columns is-centered">
<div class="column has-text-centered">
<h1 class="title is-1 publication-title">SharpDepth: Sharpening Metric Depth Predictions Using Diffusion Distillation</h1>
<h3 class="title has-text-centered">CVPR 2025</h3>
<div class="is-size-5 publication-authors">
<span class="author-block">
<a href="https://haiphamcse.github.io/" target="_blank" rel="noopener noreferrer">
Duc-Hai Pham</a><sup>*1</sup>,
</span>
<span class="author-block">
<a href="https://itsthanhtung.github.io" target="_blank" rel="noopener noreferrer">
Tung Do</a><sup>*1</sup>,
</span>
<span class="author-block">
<a href="https://phongnhhn.info/" target="_blank" rel="noopener noreferrer">
Phong Nguyen</a><sup>1</sup>,
</span>
<span class="author-block">
<a href="https://sonhua.github.io/" target="_blank" rel="noopener noreferrer">
Binh-Son Hua</a><sup>1,2</sup>,
</span>
<span class="author-block">
<a href="https://www.khoinguyen.org/" target="_blank" rel="noopener noreferrer">
Khoi Nguyen</a><sup>1</sup>,
</span>
<span class="author-block">
<a href="https://rangnguyen.github.io" target="_blank" rel="noopener noreferrer">
Rang Nguyen</a><sup>3</sup>
</span>
</div>
<div class="is-size-5 publication-authors">
<span class="author-block"> <sup>1</sup> Qualcomm AI Research <sup>2</sup> Trinity College Dublin, Ireland <sup>3</sup> VinAI, Vietnam </span>
<span class="eql-cntrb"><small><br><sup>*</sup>Indicates Equal Contribution</small></span>
</div>
<div class="column has-text-centered">
<div class="publication-links">
<!-- PDF Link. -->
<span class="link-block">
<a href="https://openaccess.thecvf.com/content/CVPR2025/html/Pham_SharpDepth_Sharpening_Metric_Depth_Predictions_Using_Diffusion_Distillation_CVPR_2025_paper.html"
class="external-link button is-normal is-rounded is-dark">
<span class="icon">
<i class="fas fa-file-pdf"></i>
</span>
<span>Paper</span>
</a>
</span>
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<a href="https://arxiv.org/abs/2411.18229"
class="external-link button is-normal is-rounded is-dark">
<span class="icon">
<i class="ai ai-arxiv"></i>
</span>
<span>arXiv</span>
</a>
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class="external-link button is-normal is-rounded is-dark">
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</div>
</section>
<section class="hero teaser">
<div class="container is-max-desktop">
<div class="hero-body">
<img id="teaser" width="100%" src="./images/Depth-teaser.svg" alt="Teaser image demonstrating Marigold depth estimation."/>
<h2 class="subtitle has-text-centered">
<span class="methodname">SharpDepth</span> bridges metric accuracy with detailed boundary preservation, resulting in depth predictions that are both metrically precise and visually sharp.
</h2>
</div>
</div>
</section>
<section class="hero">
<div class="hero-body">
<div class="container is-max-desktop">
<div class="column has-text-centered">
<h2 class="title has-text-centered is-4">Audio Presentation (by NotebookLM)</h2>
<audio controls>
<source src="images/SharpDepth_ High-Fidelity Zero-Shot Metric Depth Estimation.wav" type="audio/mpeg">
Your browser does not support the audio element.
</audio>
</div>
</div>
</div>
</section>
<!-- Teaser video-->
<section class="hero teaser">
<div class="container is-max-desktop">
<div class="hero-body">
<h2 class="title has-text-centered is-3">Video Demonstration</h2>
<video poster="" id="tree" autoplay controls muted loop height="100%">
<!-- Your video here -->
<source src="images/democlip.mp4"
type="video/mp4">
</video>
<h2 class="subtitle has-text-centered">
SharpDepth can enhance the high-frequency details of a metric depth estimator, allowing for better point-cloud reconstruction. We use gradio in this demonstration.
</h2>
</div>
</div>
</section>
<!-- End teaser video -->
<section class="section pt-0">
<div class="container is-max-desktop">
<!-- Abstract. -->
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<div class="column is-four-fifths">
<h2 class="title is-3">Overview</h2>
<div class="content has-text-justified">
<p>
We present SharpDepth, a diffusion-based depth model for refining metric depth estimators, e.g., UniDepth, without relying
on ground-truth depth data. Our method can recover sharp details in thin structures and improve overall point cloud quality.
</p>
<p>
The gallery below presents several images from the internet and a comparison of <span class="methodname">SharpDepth</span> with the previous
state-of-the-art metric depth like UniDepth. Use the slider and gestures to reveal details on both sides.
</p>
</div>
</div>
</div>
<!--/ Abstract. -->
</div>
</section>
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<!-- Method. -->
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<h2 class="title is-3">How it works</h2>
<div class="content has-text-justified">
<h3 class="title has-text-centered">
Fine-tuning protocol
</h3>
<p>
$\newcommand{\img}{\mI}
\newcommand{\depth}{\mathbf{d}}
\newcommand{\latent}{z_{i}}
\newcommand{\latentdepth}{z_{d}}
\newcommand{\discrilatentdepth}{z^{\tilde{d}_{\text{norm}}}}
\newcommand{\discridepth}{d}
\newcommand{\genedepth}{\tilde{d}}
\newcommand{\discridepthmetric}{\tilde{d}_{\text{metric}}}
\newcommand{\blendedlatent}{z^\prime_{d}}
\newcommand{\metriclatent}{z_{d}}
\newcommand{\predlatentdepth}{\hat{z}}
\newcommand{\initlatentdepth}{z^{\hat{d}_{\text{init-norm}}}}
\newcommand{\preddepth}{\hat{d}}
\newcommand{\initdepth}{d_{\text{init}}}
\newcommand{\latentimage}{z^{\mI}}
\newcommand{\noise}{\bm{\epsilon}}
\newcommand{\denoiser}{\bm{\epsilon}_{\theta}}
\newcommand{\denoiserlong}{\denoiser(\latentdepth_t, \latentimage, t)}
\newcommand{\catinput}{\mathbf{z}}
\newcommand{\encoder}{\mathcal{E}}
\newcommand{\decoder}{\mathcal{D}}
\newcommand{\discri}{f_D}
\newcommand{\genera}{f_G}
\newcommand{\refiner}{G_\theta}
\newcommand{\refinerema}{G_{\bar{\theta}}}
$
Given an input image $I$, we first use both
pre-trained metric depth model $f_D$ and diffusion-based depth model $\genera$
to produce metric and affine-invariant depth output $\discridepth$ and $\genedepth$, respectively.
Our goal is to generate a sharpened metric depth map, $\preddepth$, using our proposed sharpening model, $\refiner$. This model architecture is based on state-of-the-art pre-trained depth diffusion models.
Instead of naively relying on the forward process of diffusion model, we introduce a Noise-aware Gating mechanism, which provides explicit guidance to the sharpener $\refiner$ on uncertain regions. To enable ground-truth free fine-tuning, we use SDS loss to distill fine-grained details from the pretrained diffusion depth model $\genera$ and Noise-Aware Reconstruction Loss to ensure accurate metric prediction.
</p>
<img id="method_train" width="100%" src="./images/Depth-pipeline_crop-1.png" alt="SharpDepth training scheme"/>
<h3 class="title has-text-centered">
Comparison with other methods
</h3>
<p>
Quantitative comparison of <span class="methodname">SharpDepth</span> with SOTA metric depth estimators
on several zero-shot benchmarks. Our method achieves accuracy comparable to metric depth models.
Further evaluation on synthetic datasets (Sintel, UnrealStereo, and Spring) and a real dataset (iBims) shows that our method significantly outperforms UniDepth in both edge accuracy and completeness. By leveraging priors from the pre-trained diffusion model, our approach produces sharper depth discontinuities and achieves high accuracy across datasets.
In contrast, discriminative-based methods often produce overly smooth edges, leading to higher completeness errors.
</p>
<img id="comparison" width="100%" src="./images/quantitative_1.png" alt="Comparison with other methods"/>
<img id="comparison" width="100%" src="./images/quantitative_2.png" alt="Comparison with other methods"/>
<!-- <p class="subtitle mt-5">
Refer to the pdf paper linked above for more details on qualitative, quantitative, and ablation studies.
</p> -->
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<section class="section" id="BibTeX">
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<h2 class="title">Citation</h2>
<pre class="selectable"><code>
@article{pham2024sharpdepth,
title={SharpDepth: Sharpening Metric Depth Predictions Using Diffusion Distillation},
author={Pham, Duc-Hai and Do, Tung and Nguyen, Phong and Hua, Binh-Son and Nguyen, Khoi and Nguyen, Rang},
journal={arXiv preprint arXiv:2411.18229},
year={2024}
}
</code></pre>
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