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<h1 class="title is-1 publication-title">SceneFun3D</h1>
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<h1 class="title is-3 publication-title">Fine-Grained Functionality and Affordance Understanding<br />in 3D Scenes</h1>
<h1 class="title is-4" style="color: #5c5c5c;">CVPR 2024 (Oral)</h1>
<div class="is-size-5 publication-authors">
<span class="author-block">
<a href="https://alexdelitzas.github.io">Alexandros Delitzas</a><sup>1</sup>,
</span>
<span class="author-block">
<a href="https://aycatakmaz.github.io">Ayça Takmaz</a><sup>1</sup>,
</span>
<span class="author-block">
<a href="https://federicotombari.github.io/">Federico Tombari</a><sup>2,3</sup>,
</span>
</br>
<span class="author-block">
<a href="https://studios.disneyresearch.com/people/bob-sumner/">Robert W. Sumner</a><sup>1</sup>,
</span>
<span class="author-block">
<a href="https://people.inf.ethz.ch/pomarc/">Marc Pollefeys</a><sup>1,4</sup>,
</span>
<span class="author-block">
<a href="https://francisengelmann.github.io/">Francis Engelmann</a><sup>1,2</sup>
</span>
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<div class="is-size-5 publication-authors">
<span class="author-block" style="margin-right: 1em;"><sup>1</sup>ETH Zürich</span>
<span class="author-block" style="margin-right: 1em;"><sup>2</sup>Google</span>
<span class="author-block" style="margin-right: 1em;"><sup>3</sup>TUM</span>
<span class="author-block" style="margin-right: 1em;"><sup>4</sup>Microsoft</span>
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<span class="icon"><i class="fa fa-book" aria-hidden="true"></i></span><span>Dataset</span>
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<h2 class="subtitle has-text-centered">
We introduce <strong>SceneFun3D</strong>, a large-scale dataset with <strong>highly accurate interaction annotations</strong> in 3D real-world indoor environments.
</h2>
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<h2 class="title is-3">Abstract</h2>
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<p>
Existing 3D scene understanding methods are heavily focused on 3D semantic and instance segmentation.
However, identifying objects and their parts only constitutes an intermediate step towards a more fine-grained goal,
which is effectively interacting with the functional interactive elements (e.g., handles, knobs, buttons) in the scene to accomplish diverse tasks.
To this end, we introduce SceneFun3D, a large-scale dataset with more than 14.8k highly accurate interaction annotations for 710 high-resolution real-world 3D indoor scenes.
We accompany the annotations with motion parameter information, describing how to interact with these elements, and a diverse set of natural language descriptions of tasks that involve manipulating them in the scene context.
To showcase the value of our dataset, we introduce three novel tasks, namely functionality segmentation, task-driven affordance grounding and 3D motion estimation, and adapt existing state-of-the-art methods to tackle them.
Our experiments show that solving these tasks in real 3D scenes remains challenging despite recent progress in closed-set and open-set 3D scene understanding.
</p>
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<h2 class="title is-3">Motivation</h2>
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<p>
Existing 3D scene understanding methods are <strong>heavily</strong> focused on understanding the scene on a <strong>coarse object level</strong>
by detecting or segmenting the 3D object instances. However, identifying 3D objects is only an intermediate step towards a more fine-grained goal. In real-world applications, agents need to <strong>interact with the
functional interactive elements</strong> (e.g., handles, knobs, buttons) and <strong>reason about their purpose in the scene context</strong> to successfully complete tasks.
</p>
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<h2 class="title is-3">The <b>SceneFun3D</b> dataset</h2>
<!-- <img src="./static/images/framework.png" class="teaser-fig" alt="teaser-fig."> -->
<div class="content has-text-justified">
<p>
We introduce SceneFun3D, the first large-scale dataset with <strong>geometrically fine-grained interaction annotations</strong> in 3D real-world indoor environments.
We aim to encourage research on the following questions:
<ul>
<li><strong>Where</strong> are the functionalities located in 3D indoor environments and what actions they afford?</li>
<li><strong>What</strong> purpose do the functionalities serve in the scene context?</li>
<li><strong>How</strong> to interact with the functional elements?</li>
</ul>
</p>
</div>
<br />
<h3 class="title is-4">Functional interactive elements</h3>
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<br /><br />
<div class="content has-text-justified">
<p>
SceneFun3D contains more than <strong>14.8k annotations of functional interactive elements</strong> in <strong>710 high-fidelity reconstructions of indoor environments</strong>.
These annotations comprise a 3D instance mask followed by an affordance label. We define <strong>nine affordance categories</strong>
to describe interactions afforded by common scene functionalities.
</p>
</div>
<br />
<h3 class="title is-4">Natural language task descriptions</h3>
<img src="./static/images/desc_ex_v6.jpg" class="teaser-fig" alt="teaser-fig.">
<br /><br />
<div class="content has-text-justified">
<p>
Beyond localizing the functionalities, it is crucial to understand the purpose that they serve in the scene context.
To this end, we collect <strong>free-form diverse language descriptions of tasks</strong> that involve interacting with the scene functionalities.
</p>
</div>
<br />
<h3 class="title is-4">3D motions</h3>
<img src="./static/images/motion_ex_v4.jpg" class="teaser-fig" alt="teaser-fig.">
<br /><br />
<div class="content has-text-justified">
<p>
To achieve holistic scene understanding, we collect <strong>annotations of the motions</strong> required to manipulate the interactive elements.
</p>
</div>
</div>
</div>
</div>
</section>
<section class="section">
<div class="container is-max-desktop">
<div class="columns is-centered has-text-centered">
<div class="column">
<h2 class="title is-3">Novel 3D Scene Understanding tasks</h2>
<!-- <img src="./static/images/framework.png" class="teaser-fig" alt="teaser-fig."> -->
<div class="content has-text-justified">
<p>
We introduce <strong>three novel 3D scene understanding tasks</strong>, namely functionality segmentation, task-driven affordance grounding and 3D motion estimation.
Additionally, we propose <strong>closed- and open-vocabulary methods</strong> to tackle them and perform systematic benchmarking.
</p>
</div>
<br />
<h3 class="title is-4">Task 1: Functionality segmentation</h3>
<video controls loop poster="static/images/task1_pic.png">
<source src="static/images/task1_vid.mp4" type="video/mp4">
</video>
<br /><br />
<div class="content has-text-justified">
<p>
Given a 3D point cloud of a scene, the goal is to segment the functional interactive element instances and predict the associated affordance labels.
</p>
</div>
<br />
<h3 class="title is-4">Task 2: Task-driven affordance grounding</h3>
<video controls loop poster="static/images/task2_pic.png">
<source src="static/images/task2_vid.mp4" type="video/mp4">
</video>
<br /><br />
<div class="content has-text-justified">
<p>
Given a language task description (e.g., “open the fridge”), the goal is to predict
the instance mask of the functional element that we need to interact with and the label of the action it affords.
</p>
</div>
<br />
<h3 class="title is-4">Task 3: 3D motion estimation</h3>
<video controls loop poster="static/images/task3_pic.png">
<source src="static/images/task3_vid.mp4" type="video/mp4">
</video>
<br /><br />
<div class="content has-text-justified">
<p>
In addition to segmenting the functionalities, the goal is to infer the motion parameters
which describe how an agent can interact with the predicted functionalities.
</p>
</div>
</div>
</div>
</div>
</section>
<section class="section">
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<h2 class="title is-3">Applications</h2>
<img src="./static/images/potential_applications.png" class="teaser-fig" alt="teaser-fig.">
<div class="content has-text-justified">
<p>
Our aim is to <strong>catalyze research</strong> on robotic systems, embodied AI and AR/VR applications.
In robotics (left), localizing visual affordances and grounding them to natural language task
descriptions is a crucial skill for embodied intelligent agents. By providing accurate 3D affordance masks,
we facilitate the generation of realistic human-scene interactions (right), unleashing new directions in virtual human applications.
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<h2 class="title">BibTeX</h2>
<pre><code>@inproceedings{delitzas2024scenefun3d,
title = {{SceneFun3D: Fine-Grained Functionality and Affordance Understanding in 3D Scenes}},
author = {Delitzas, Alexandros and Takmaz, Ayca and Tombari, Federico and Sumner, Robert and Pollefeys, Marc and Engelmann, Francis},
booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
year = {2024}
}</code></pre>
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