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<h1 class="title is-2 publication-title">Unifying 3D Vision-Language Understanding via Promptable Queries</h1>
<div class="is-size-4"><b>ECCV 2024</b></div>
<div class="is-size-5 publication-authors">
<span class="author-block">
<a href="https://zhuziyu-edward.github.io">Ziyu Zhu</a><sup>1,2</sup>
<a href="https://tongclass.ac.cn/author/zhuofan-zhang/">Zhuofan Zhang</a><sup>1,2</sup>
<a href="https://jeasinema.github.io">Xiaojian Ma</a><sup>2</sup>
<a href="https://nxsedson.github.io">Xuesong Niu</a><sup>2</sup>
<a href="https://yixchen.github.io">Yixin Chen</a><sup>2</sup>
<a href="https://buzz-beater.github.io">Baoxiong Jia</a><sup>2</sup>
<br>
<a href="https://www.cs.tsinghua.edu.cn/csen/info/1165/4052.htm">Zhidong Deng</a><sup>1📧</sup>
<a href="https://siyuanhuang.com/">Siyuan Huang</a><sup>2📧</sup>
<a href="https://liqing.io/">Qing Li</a><sup>2📧</sup>
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<!-- <span class="author-block">✉️ corresponding author</span> <br> -->
<span class="author-block"><sup>1</sup>Tsinghua University</span>
<span class="author-block"><sup>2</sup>Beijing Institute for General Artificial Intelligence (BIGAI)</span> <br>
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<h2 class="subtitle has-text-justified">
<b>TL;DR:</b> We propose PQ3D, a unfied model for 3D vision-language understanding, capable of taking various prompts and representations to perform a wide range of tasks in a 3D Scene.
</h2>
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src="https://www.youtube.com/embed/XRxnEpgeljE?si=v1QTDo1d_hc6wHi1">
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<h2 class="title is-3">Abstract</h2>
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<p>
A unified model for 3D vision-language (3D-VL) understanding is expected to take various scene representations and perform a wide
range of tasks in a 3D scene. However, a considerable gap exists between existing methods and such a unified model, due to the independent application of representation and insufficient exploration of 3D multi-task
training. In this paper, we introduce PQ3D, a unified model capable of using Promptable Queries to tackle a wide range of 3D-VL tasks,
from low-level instance segmentation to high-level reasoning and planning.Tested across ten diverse 3D-VL datasets,
</p>
<p>
This is achieved through three key innovations: (1) unifying various 3D scene representations (i.e., voxels, point clouds, multi-view im-
ages) into a shared 3D coordinate space by segment-level grouping, (2) an attention-based query decoder for task-specific information retrieval
guided by prompts, and (3) universal output heads for different tasks to support multi-task training.
</p>
<p>
PQ3D demonstrates impressive performance on these tasks, setting new records on most benchmarks. Particularly, PQ3D improves the state-
of-the-art on ScanNet200 by 4.9% (AP25), ScanRefer by 5.4% ([email protected]), Multi3DRefer by 11.7% ([email protected]), and Scan2Cap by 13.4% ([email protected]).Moreover, PQ3D supports flexible inference with individual or combined forms of available 3D representations, e.g., solely voxel input
</p>
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<h2 class="title is-3">Contribution</h2>
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<p>
Our main contributions are
</p><ol type="1">
<li><b>PQ3D model</b><span style="font-size: 95%;"></span> We propose a unified model adept at handling a broad spectrum of 3D vision-language tasks, rang
ing from low-level instance segmentation to high-level
reasoning and planning.</span></li>
<li><b>Representations alignment</b>. <span style="font-size: 95%;"> Our model uniquely aligns voxels, point clouds, and multi-view images into a
shared 3D space and employs an attention-based query decoder to adaptively extract task-relevant features guided by prompts, offering a flexible approach to model all 3D-VL tasks.</span></li>
<li><b>Performance</b>. <span style="font-size: 95%;">In our extensive experimentation across various 3D-VL tasks, PQ3D not only achieves competitive results but also sets new records in most of the tasks
</span></li>
</ol>
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<h2 class="title is-3"> <img id="painting_icon" width="4%" src="https://img.icons8.com/?size=1x&id=oOOSYZyuA844&format=png">PQ3D Model</h2>
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<h2 class="title is-3"> <img id="painting_icon" width="4%" src="https://img.icons8.com/?size=1x&id=sei9JmRv5YVb&format=png"> Examples on 3D VL Understanding </h2>
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<p>
Qualitative results on promptable segmentation, visual grounding, question answering, dense captioning, object navigation, and task planning.
</p>
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<h2 class="title is-3"> <img id="painting_icon" width="4%" src="https://img.icons8.com/?size=1x&id=43611&format=png"> Promptable Segmentation Results</h3>
<hr style="margin-top:0px">
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<iframe style="clip-path: inset(1px 1px)" width="100%" height="100%" src="https://sketchfab.com/playlists/embed?collection=25b7b3544c884c9e8f0ca319c1950bc8&autostart=0" frameborder="0" allow="accelerometer; autoplay; encrypted-media; gyroscope; picture-in-picture; fullscreen" mozallowfullscreen="true" webkitallowfullscreen="true"></iframe>
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<br>
<section class="section" id="BibTeX">
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<h2 class="title">BibTeX</h2>
<pre><code>@article{zhu2024unifying,
title={Unifying 3D Vision-Language Understanding via Promptable Queries},
author={Zhu, Ziyu and Zhang, Zhuofan and Ma, Xiaojian and Niu, Xuesong and Chen, Yixin and Jia, Baoxiong and Deng, Zhidong and Huang, Siyuan and Li, Qing},
journal={ECCV},
year={2024}
}</code></pre>
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