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<section class="hero">
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<h1 class="title is-1 publication-title">D<sup>3</sup>RoMa: Disparity Diffusion-based Depth Sensing for Material-Agnostic Robotic Manipulation</h1>
<div class="is-size-5 publication-authors">
<!-- Paper authors -->
<!-- <span class="author-block"><a href="FIRST AUTHOR PERSONAL LINK" target="_blank">Jiazhao Zhang</a><sup>1,2,*</sup>,</span> -->
<span class="author-block">
<a href="https://songlin.github.io/">Songlin Wei</a><sup>1,4,</sup>
<a href="https://geng-haoran.github.io/">Haoran Geng</a><sup>2,3</sup>
<a href="https://jychen18.github.io/">Jiayi Chen</a><sup>1,4</sup>
<a href="https://cs.stanford.edu/~congyue/">Congyue Deng</a><sup>3</sup>
Wenbo Cui<sup>5,6</sup>
<a href="https://chengyzhao.github.io/">Chengyang Zhao</a><sup>1,4</sup>
Xiaomeng Fang<sup>6</sup>,
<a href="https://geometry.stanford.edu/member/guibas/">Leonidas Guibas</a><sup>3</sup>
<a href="https://hughw19.github.io/">He Wang</a><sup>1,4,6</sup>
</span>
</div>
<div class="is-size-5 publication-authors">
<!-- <span class="author-block">Institution Name<br>Conferance name and year</span> -->
<span class="author-block"><sup>1</sup>
Peking University
<sup>2</sup>University of California, Berkeley
<sup>3</sup>Stanford University
</span>
<span class="author-block" style="display: block;">
<sup>4</sup>Galbot
<sup>5</sup>Univsersity of Chinese Academy of Sciences
<sup>6</sup>Beijing Academy of Artificial Intelligence
</span>
</div>
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<div class="publication-links">
<!-- Arxiv PDF link -->
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class="external-link button is-normal is-rounded is-dark">
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<i class="fas fa-file-pdf"></i>
</span>
<span>Open Review</span>
</a>
</span>
<!-- Arxiv PDF link -->
<span class="link-block">
<a href="https://arxiv.org/abs/2409.14365" target="_blank"
class="external-link button is-normal is-rounded is-dark">
<span class="icon">
<i class="ai ai-arxiv"></i>
</span>
<span>Arxiv</span>
</a>
</span>
<!-- Supplementary PDF link -->
<span class="link-block">
<a href="https://youtu.be/ZMPeBjiEdEo" target="_blank"
class="external-link button is-normal is-rounded is-dark">
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<i class="fas fa-file-video"></i>
</span>
<span>Video</span>
</a>
</span>
<!--Github link -->
<span class="link-block">
<a href="https://github.com/songlin/d3roma" target="_blank"
class="external-link button is-normal is-rounded is-dark">
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<i class="fab fa-github"></i>
</span>
<span>Code</span>
</a>
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<a href="#" target="_blank"
class="external-link button is-normal is-rounded is-dark">
<span class="icon">
<i class="fab fa-file-download"></i>
</span>
<span>Dataset</span>
</a>
</span> -->
<!-- ArXiv abstract Link -->
<!-- <span class="link-block">
<a href="https://arxiv.org/abs/2402.15852" target="_blank"
class="external-link button is-normal is-rounded is-dark">
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<span>Paper</span>
</a>
</span> -->
</div>
</div>
<div class="text">
8th Conference on Robot Learning (CoRL 2024), Munich, Germany.
</div>
</div>
</div>
</div>
</div>
</section>
<!--<section class="hero is-small">
<div class="hero-body">
<div class="container is-max-desktop has-text-centered">
<h2>
<h2 class=""> <img src="static/images/highlight_logo.png" width="50"> Highlights</h2>
<div class="text-image-container title is-3">
<div>
<img src="static/images/highlight_logo.png" alt="示例图片" width="50">
</div>
<div class="text">
<p>Highlights</p>
</div>
</div>
</h2>
<p> <b>Real-world demos by following simple instructions, such as walking to a single landmark.</b></p>
<ul>
<li><b>NaVid is the first video-based Vision-Language Model (VLM) for the task of vision-and-language navigation (VLN).</b></li>
<li><b>NaVid navigates in a human-like manner, requiring solely an on-the-fly video stream from a monocular camera as input, without the need for maps, odometers, or depth inputs.</b></li>
<li><b>NaVid incorporates 510K VLN video sequences from simulation environments and 763K real-world caption samples to achieve cross-scene generalization.</b></li>
<li><b>NaVid is co-trained with real-world caption data (763k) and simulated VLN data (510k). The VLN capability is obtained by leveraging simulation environments, while real-world understanding is gained through real-world caption data.</b></li>
<li><b>NaVid achieves state-of-the-art (SOTA) performance in both simulated and real-world environments, and exhibits strong generalizability on unseen scenarios.</b></li>
</ul>
</div>
</div>
</section> -->
<!-- Teaser video 1 -->
<section class="hero is-small">
<div class="hero-body">
<div class="container is-max-desktop has-text-centered">
<h2 class="title is-5">Depth Estimation: For transparent and reflective objects</h2>
<p> <b>In these demos, we compare the depth predicted by D3RoMa with raw sensor depths captured by RealSense D415/D435.</b></p>
<div id="results-carousel-teaser1" class="carousel results-carousel">
<div class="item item-video">
<video poster="" id="video1" autoplay playsinline controls muted loop height="100%">
<source src="static/videos/depth_restore/1.mp4"
type="video/mp4">
</video>
</div>
<div class="item item-video">
<video poster="" id="video1" autoplay playsinline controls muted loop height="100%">
<source src="static/videos/depth_restore/2.mp4"
type="video/mp4">
</video>
</div>
<div class="item item-video">
<video poster="" id="video2" autoplay playsinline controls muted loop height="100%">
<source src="static/videos/depth_restore/3.mp4"
type="video/mp4">
</video>
</div>
<!-- <div class="item item-video"></div>
<video poster="" id="video3" autoplay playsinline controls muted loop height="100%">
<source src="static/videos/depth_restore/4.mp4"
type="video/mp4">
</video>
</div>
<div class="item item-video"></div>
<video poster="" id="video4" autoplay playsinline controls muted loop height="100%">
<source src="static/videos/depth_restore/5.mp4"
type="video/mp4">
</video>
</div> -->
</div>
</div>
</div>
</section>
<!-- End teaser video 1 -->
<!-- Paper abstract -->
<section class="section hero is-light">
<div class="container is-max-desktop">
<div class="columns is-centered has-text-centered">
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<h2 class="title is-3">Abstract</h2>
<div class="content has-text-justified">
<p>
Depth sensing is an important problem for 3D vision-based robotics.
Yet, a real-world active stereo or ToF depth camera often produces noisy and in-
complete depth which bottlenecks robot performances. In this work, we propose
D3RoMa, a learning-based depth estimation framework on stereo image pairs that
predicts clean and accurate depth in diverse indoor scenes, even in the most challenging scenarios
with translucent or specular surfaces where classical depth sensing completely fails.
Key to our method is that we unify depth estimation and restoration into an image-to-image translation problem by predicting the disparity map with a denoising diffusion probabilistic model. At inference time, we
further incorporated a left-right consistency constraint as classifier guidance to
the diffusion process. Our framework combines recently advanced learning-based
approaches and geometric constraints from traditional stereo vision. For model
training, we create a large scene-level synthetic dataset with diverse transparent
and specular objects to compensate for existing tabletop datasets. The trained
model can be directly applied to real-world in-the-wild scenes and achieve state-of-the-art performance in multiple public depth estimation benchmarks.
Further experiments in real environments show that accurate depth prediction significantly
improves robotic manipulation in various scenarios.
</p>
</div>
</div>
</div>
</div>
</section>
<!-- End paper abstract -->
<!-- YouTube Video-->
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<h2 class="title is-3">Supplementary Video</h2>
<div class="publication-video">
<!---TODO Dhruv: put video link here-->
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allow="autoplay; encrypted-media" allowfullscreen></iframe> -->
<!-- <iframesrc="https://www.youtube.com/embed/ZMPeBjiEdEo?si=Jttt7J_tVZnz5HnB" title="YouTube video player"
frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share"
referrerpolicy="strict-origin-when-cross-origin" allowfullscreen>
</iframe> -->
<iframe width="560" height="315" src="https://www.youtube.com/embed/ZMPeBjiEdEo?si=Jttt7J_tVZnz5HnB&start=31" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" referrerpolicy="strict-origin-when-cross-origin" allowfullscreen></iframe>
</div>
</div>
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</section>
<!-- Method Overview -->
<section class="hero is-small">
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<h2 class="title is-3">Method Overview</h2>
<img src="static/images/method.png" alt="NaVid" class="center-image blend-img-background">
<div class="level-set has-text-justified">
<p class="has-text-justified">
<b>Disparity diffusion with stereo-geometry guidance.</b>Our disparity diffusion-based depth
sensing framework takes the raw disparity map ˜D and the left-right stereo image pair Il, Ir as input.
With the geometry prior from the stereo matching between Il and Ir as guidance for the reverse
sampling, our diffusion model can gradually perform the denoising process conditioned on ˜D to
predict the restored disparity map x<sub>0</sub>.
</p>
</div>
</div>
</div>
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</section>
<section class="hero is-small">
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<h2 class="title is-3">In the Wild Depth Predictions</h2>
<img src="static/images/in-the-wild-new.png" alt="In the wild" class="center-image blend-img-background">
<div class="level-set has-text-justified">
<p class="has-text-justified">
<b>Generalizability of D3RoMa in the real world. </b>Our method robustly predicts transparent
(bottles) and specular (basin and cups) object depths in tabletop environments and beyond.
RGB image, pseudo colorized raw disparity map, our prediction, and point cloud are displayed for each case of a total of 6 frames captured by camera RealSense D415 and D435.
* RGB and depth images are not aligned for the D435 camera for better visualization.
</p>
<p class="has-text-justified">
<!-- <b>We initialize the encoders and Vicuna-7B using pre-trained weights, and our model requires only one epoch for the training process.</b> -->
</p>
</div>
</div>
</div>
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</section>
<section class="hero is-small"></section>
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<div class="container is-max-desktop">
<div class="columns is-centered has-text-centered">
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<h2 class="title is-3">Generalization comparisons with State-of-the-art monocular depth estimation methods.</h2>
<img src="static/images/mde.png" alt="In the wild" class="center-image blend-img-background">
<div class="level-set has-text-justified">
<p class="has-text-justified">
<b>Generalization comparisons with State-of-the-art monocular depth estimation methods.
</b> All the results except ours are taken from their official web demo. Different methods used different
color maps. We found that most monocular methods produce inferior quality depth even without
considering the absolute scale.
</p>
<p class="has-text-justified">
<!-- <b>We initialize the encoders and Vicuna-7B using pre-trained weights, and our model requires only one epoch for the training process.</b> -->
</p>
</div>
</div>
</div>
</div>
</div>
</div>
</section>
<section class="hero is-small"></section>
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<h2 class="title is-3"></h2>
<div class="item item-video">
<video poster="" id="video2" autoplay playsinline controls muted loop height="100%">
<source src="static/videos/depth_restore/depth_anything_2.mp4"
type="video/mp4">
</video>
</div>
</div>
</div>
</div>
</div>
</div>
</section>
<!--BibTex citation -->
<section class="section" id="BibTeX">
<div class="container is-max-desktop content">
<h2 class="title">BibTeX</h2>
<pre><code>@inproceedings{
wei2024droma,
title={D3RoMa: Disparity Diffusion-based Depth Sensing for Material-Agnostic Robotic Manipulation},
author={Songlin Wei and Haoran Geng and Jiayi Chen and Congyue Deng and Cui Wenbo and Chengyang Zhao and Xiaomeng Fang and Leonidas Guibas and He Wang},
booktitle={8th Annual Conference on Robot Learning},
year={2024},
url={https://openreview.net/forum?id=7E3JAys1xO}
}</code></pre>
</div>
</section>
<!--End BibTex citation -->
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