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<!DOCTYPE html>
<html>
<head>
<meta charset="utf-8">
<meta name="description"
content="Holistic Urban 3D Scene Understanding via Gaussian Splatting">
<meta name="keywords" content="holistic, urban, understanding, gaussian, reconstruction">
<meta name="viewport" content="width=device-width, initial-scale=1">
<title>UrbanCAD: Towards Highly Controllable and Photorealistic 3D Vehicles for Urban Scene Simulation</title>
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<h1 class="title is-1 publication-title">UrbanCAD: Towards Highly Controllable and Photorealistic
3D Vehicles for Urban Scene Simulation</h1>
<div class="is-size-5 publication-authors">
<span class="author-block">
<a href="https://yichonglu.github.io">Yichong Lu</a><sup>1*</sup>,</span>
<span class="author-block">
Yichi Cai</a><sup>1*</sup>,</span>
<span class="author-block">
<a href="https://zhanghe3z.github.io/">Shangzhan Zhang</a><sup>1</sup>,
</span>
<span class="author-block">
<a href="https://github.com/hyzhou404">Hongyu Zhou</a><sup>1</sup>,
</span>
<span class="author-block">
<a href="https://scholar.google.com/citations?user=kFnuXyAAAAAJ&hl=zh-TW">Haoji Hu</a><sup>1</sup>,
</span>
<span class="author-block">
Huimin Yu</a><sup>1</sup>,
</span>
<span class="author-block">
<a href="https://www.cvlibs.ne/">Andreas Geiger</a><sup>2,3</sup>
</span>
<span class="author-block">
<a href="https://yiyiliao.github.io">Yiyi Liao</a><sup>1</sup>
</span>
</div>
<div class="is-size-5 publication-authors">
<span class="author-block"><sup>1</sup>Zhejiang University,</span>
<span class="author-block"><sup>2</sup>University of Tübingen,</span>
<span class="author-block"><sup>3</sup>Tübingen AI Center</span>
</div>
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</div>
</section>
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<h2 class="title is-3">Overview</h2>
<img id="teaser" width="100%" src="./images/teaser.png" alt="Teaser image demonstrating Marigold depth estimation."/>
<div class="content has-text-justified">
<p>
We present <span class="methodname">UrbanCAD</span>, a novel pipeline that automatically builds photorealistic and highly controllable digital twins from a single urban image and a large
collection of 3D CAD models and handcrafted materials, supporting various editing operations (top). The produced CAD models can be
photorealistically inserted into various background scenes and rendered in novel views, synthesizing challenging out-of-distribution (OOD)
scenarios with high fidelity for important downstream applications (bottom).
</p>
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<h2 class="title is-3">Comparison with baselines</h2>
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<!-- <div class="content has-text-justified">
<p>
UrbanCAD produces more robust and realistic results especially at the novel viewpoint compared to the baselines.
</p>
</div> -->
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</section>
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<div class="container is-max-desktop">
<!-- Method. -->
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<div class="column is-five-fifths">
<h2 class="title is-3">How it works</h2>
<div class="content has-text-justified">
<p>
Given a single view input image, we first perform CAD model retrieval and retrieval-based material optimization to
create photorealistic and highly controllable vehicle digital twins (left). Given multi-view background images, we then perform realistic
vehicle insertion to create various synthetic data for self-driving system testing (right).
</p>
<img id="method" width="100%" src="./images/pipeline.png" alt="Marigold training scheme"/>
</div>
</div>
</div>
<!--/ Method. -->
</div>
</section>
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<section class="section">
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<div class="columns is-centered has-text-centered">
<div class="column is-five-fifths">
<h2 class="title is-3">Downstream Applications</h2>
<div class="content has-text-justified">
<h3 class="title is-4">Constructing photorealistic urban scenarios</h3>
<p>
We find our CAD retrieval, material optimization, and lighting estimation modules are all crucial to constructing photorealistic urban scenarios with smaller domain gaps by testing on various perception models.
</p>
<img id="method" width="100%" src="./images/downstream_1.png" alt="Marigold training scheme"/>
<h3 class="title is-4">Constructing OOD situations for perception model testing</h3>
<p>
Thanks to the high controllability of our generated vehicle models, we can construct various out-of-distribution situations to test the robustness of autonomous perception models. We find the performance of perception models degrades obviously when tested on our constructed door opening scenarios.
</p>
<img id="ood_door" width="100%" src="./images/ood/ood_door.png" alt="Marigold training scheme"/>
<img id="method2" width="100%" src="./images/downstream_2.png" alt="Marigold training scheme"/>
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<br>
<h3 class="title has-text-centered">
More Reconstruction Results
</h3>
<section class="section">
<div class="container is-max-desktop">
<div class="columns is-centered">
<div class="column">
<div class="content">
<img id="img_21" width="100%" src="./images/more_pairs_images/21.png" alt="Marigold training scheme"/>
</div>
</div>
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<div class="content">
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<source src="./video/more_pairs_video/scene_21.mp4"
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</video>
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</div>
<div class="column">
<div class="content">
<img id="img_27" width="100%" src="./images/more_pairs_images/27.png" alt="Marigold training scheme"/>
</div>
</div>
<div class="column">
<div class="content">
<video id="scene_27" autoplay controls muted loop playsinline height="100%">
<source src="./video/more_pairs_video/scene_27.mp4"
type="video/mp4">
</video>
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</div>
</div>
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<div class="column">
<div class="content">
<img id="img_28" width="100%" src="./images/more_pairs_images/28.png" alt="Marigold training scheme"/>
</div>
</div>
<div class="column">
<div class="content">
<video id="scene_28" autoplay controls muted loop playsinline height="100%">
<source src="./video/more_pairs_video/scene_28.mp4"
type="video/mp4">
</video>
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</div>
<div class="column">
<div class="content">
<img id="img_38" width="100%" src="./images/more_pairs_images/38.png" alt="Marigold training scheme"/>
</div>
</div>
<div class="column">
<div class="content">
<video id="scene_38" autoplay controls muted loop playsinline height="100%">
<source src="./video/more_pairs_video/scene_38.mp4"
type="video/mp4">
</video>
</div>
</div>
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<div class="columns is-centered">
<div class="column">
<div class="content">
<img id="img_3581" width="100%" src="./images/more_pairs_images/3581.png" alt="Marigold training scheme"/>
</div>
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<source src="./video/more_pairs_video/scene_4046.mp4"
type="video/mp4">
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<div class="columns is-centered">
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<img id="img_4947" width="100%" src="./images/more_pairs_images/4947.png" alt="Marigold training scheme"/>
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<img id="img_8240" width="100%" src="./images/more_pairs_images/8240.png" alt="Marigold training scheme"/>
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<img id="img_9664" width="100%" src="./images/more_pairs_images/9664.png" alt="Marigold training scheme"/>
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<h2 class="title">BibTeX</h2>
<pre><code>@misc{lu2024urbancadhighlycontrollablephotorealistic,
title={UrbanCAD: Towards Highly Controllable and Photorealistic 3D Vehicles for Urban Scene Simulation},
author={Yichong Lu and Yichi Cai and Shangzhan Zhang and Hongyu Zhou and Haoji Hu and Huimin Yu and Andreas Geiger and Yiyi Liao},
year={2024},
eprint={2411.19292},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2411.19292},
}</code></pre>
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