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<!DOCTYPE html>
<html>
<head>
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content="SparseGrasp">
<meta name="keywords" content="Feature, 3DGS, 3D Gaussian Splatting, Distillation, Radiance Fields, Feature Fields">
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<title>SparseGrasp: Robotic Grasping via 3D Semantic Gaussian Splatting from Sparse Multi-View RGB Images</title>
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<!-- <h1 class="title is-1 publication-title">Feature 3DGS: Supercharging 3D Gaussian Splatting to Enable Distilled Feature Fields</h1> -->
<h1 class="title is-1 publication-title">SparseGrasp</h1>
<h1 class="title is-2 publication-title">Robotic Grasping via 3D Semantic Gaussian Splatting from Sparse Multi-View RGB Images</h1>
<style>
.author-block {
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}
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<div class="is-size-5 publication-authors">
<span class="author-block">
<a href="">Junqiu Yu</a><sup>1*</sup>,</span>
<span class="author-block">
<a href="">Xinlin Ren</a><sup>1*</sup>,</span>
<span class="author-block">
<a href="">Yongchong Gu</a><sup>1</sup>,</span>
<span class="author-block">
<a href="">Haito Lin</a><sup>1</sup>,</span>
<span class="author-block">
<a href="">Tianyu Wang</a><sup>1</sup>,</span>
<span class="author-block">
<a href="">Yi Zhu</a><sup>2</sup>,</span>
<span class="author-block">
<a href="">Hang Xu</a><sup>2</sup>,</span>
<span class="author-block">
<a href="">Yugang Jiang</a><sup>1</sup>,</span>
<span class="author-block">
<a href="">Xiangyang Xue</a><sup>1</sup>,</span>
<span class="author-block">
<a href="">Yanwei Fu</a><sup>1</sup>,</span>
</span>
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<span class="author-block"><sup>1</sup>University of California, Los Angeles</span>
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<div class="is-size-5 publication-authors">
<span class="author-block"><sup>2</sup>University of Texas at Austin</span>
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<span class="author-block"><sup>3</sup>DEVCOM Army Research Laboratory</span>
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<span class="univerity-block"><sup>1</sup>Fudan University</span>
<span class="univerity-block"><sup>2</sup>Department of Noah's Ark Lab, Huawei Technology</span>
</div>
<div class="is-size-6 publication-authors">
<span class="author-block">*Denotes Equal Contribution</span>
</div>
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<span class="univerity-block"><b>CVPR 2024, Seattle</b></span>
<span class="univerity-block">CVPR 2024, Seattle</span>
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<font color="#753422"><b>g</b></font>
<font color="#FFD523"><b>h</b></font>
<font color="#71EFA3"><b>l</b></font>
<font color="#0F52BA"><b>i</b></font>
<font color="#66CC66"><b>g</b></font>
<font color="#FF9966"><b>h</b></font>
<font color="#FFCCCC"><b>t</b></font>
<font color="#00C1D4">k</font>
<font color="#EFE3D0">s</font>
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<span>Paper</span>
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<!-- <h2 class="title is-3 has-text-centered">Approach</h2> -->
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</figure>
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<p>
SparseGrasp reconstructs scenes from sparse-view RGB images and directly distills semantic features
into Gaussians, enabling open-vocabulary scene understanding. Additionally, it efficiently handles scene updates
and introduces a method for generating grasp poses directly from 3D Gaussians
</p>
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</div>
</section>
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<h2 class="title is-3">Abstract</h2>
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<p>
Language-guided robotic grasping is a rapidly advancing field where robots are instructed using human language to grasp specific objects. However, existing methods
often depend on dense camera views and struggle to
quickly update scenes, limiting their effectiveness in changeable
environments. In contrast, we propose SparseGrasp, a novel
open-vocabulary robotic grasping system that operates efficiently with sparse-view RGB images and handles scene updates
fastly. Our system builds upon and significantly enhances existing computer vision modules in robotic learning. Specifically,
SparseGrasp utilizes DUSt3R to generate a dense point
cloud as the initialization for 3D Gaussian Splatting (3DGS),
maintaining high fidelity even under sparse supervision. Importantly, SparseGrasp incorporates semantic awareness from
recent vision foundation models. To further improve processing efficiency, we repurpose Principal Component Analysis
(PCA) to compress features from 2D models. Additionally, we
introduce a novel render-and-compare strategy that ensures
rapid scene updates, enabling multi-turn grasping in changeable
environments. Experimental results show that SparseGrasp
significantly outperforms state-of-the-art methods in terms of
both speed and adaptability, providing a robust solution for
multi-turn grasping in changeable environment.
</p>
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<p>...</p>
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<p>
Our method starts with collecting sparse view images and generating dense point clouds to initialize 3DGS.
Next, we integrate FastSAM and MaskCLIP to generate average features within each mask. Then, PCA is applied to compress
the whole average features in a low dimension, then distilled into 3DGS. Given an open-vocabulary language instruction,
our system can locate the target object and generate appropriate grasp poses. When scene changes, the Render-and-Compare
strategy enables fast scene updates
</p>
</div>
</div>
</section>
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<h3 class="title is-3">Extracting Good Visual Features from Sparse Views</h3>
<div class="content has-text-justified">
<p>
SparseGrasp integrates DUSt3R to generate a dense point cloud from sparse views as the scene's initialization.
For dense visual feature extraction, it leverages MaskCLIP for patch-level alignment and FastSAM to produce masks
with clear object boundaries, resolving overlaps by prioritizing smaller regions.
This process results in a dense feature matrix with precise pixel-level semantics,
achieved efficiently in approximately 180ms.
<br>
</p>
</div>
<div class="columns is-centered">
<div class="columns is-centered">
<div class="column">
<h2 class="title is-6">Original Gaussian Splatting</h2>
<div>
<img src="./media/extract/colmap_init.gif">
</div>
</div>
<div class="column">
<h2 class="title is-6">SparseGrasp</h2>
<div>
<img src="./media/extract/dust3r_init.gif">
</div>
</div>
</div>
</div>
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left: 4px;
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</style>
</div>
</section>
<section class="section">
<div class="container is-max-desktop">
<h3 class="title is-3">3D Semantic Gaussian Splatting by Distilling Semantics</h3>
<div class="content has-text-justified">
<p>
SparseGrasp facilitates semantic feature distillation through enhanced differentiable rasterization
and achieves real-time feature rendering by leveraging PCA for efficient feature compression.
<br>
</p>
</div>
<div class="columns is-centered">
<div class="columns is-centered">
<div class="column">
<h2 class="title is-6">grape</h2>
<div>
<img src="./media/distill/grape.gif">
</div>
</div>
<div class="column">
<h2 class="title is-6">Baymax</h2>
<div>
<img src="./media/distill/baymax.gif">
</div>
</div>
</div>
<style>
.before,
.after {
margin: 0;
}
.before figcaption,
.after figcaption {
background: #fff;
border: 1px solid #c0c0c0;
border-radius: 4px;
color: #2e3452;
opacity: 0.8;
padding: 4px;
position: absolute;
top: 50%;
transform: translateY(-50%);
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</div>
</section>
<section class="section">
<div class="container is-max-desktop">
<h3 class="title is-3">Render and Compare for Fast Scene Updating</h3>
<div class="columns is-centered">
<figure class="image is-centered">
<img src="./media/render_and_compare/dynamic.jpg" alt="Pipeline Image">
</figure>
</div>
<div class="content has-text-justified">
<p>
The proposed method handles changeable environments by using sparse-view images to predict objects' translations
and rotations, which serve as optimization parameters. Moved pixels are detected using the MOG2 algorithm, their
semantic features are used to identify object's 3D Gaussians, and optimization adjusts these Gaussians while rendering updated images.
This efficient process, completing in ~200 ms.
</p>
</div>
<!--/ Matting. -->
</div>
<style>
.before,
.after {
margin: 0;
}
.before figcaption,
.after figcaption {
background: #fff;
border: 1px solid #c0c0c0;
border-radius: 4px;
color: #2e3452;
opacity: 0.8;
padding: 4px;
position: absolute;
top: 50%;
transform: translateY(-50%);
line-height: 100%;
}
.before figcaption {
left: 4px;
}
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right: 4px;
}
</style>
</div>
</section>
<section class="section">
<div class="container is-max-desktop">
<h3 class="title is-3"> Language-guided Robotic Grasping</h3>
<div class="columns is-centered">
<figure class="image is-centered" style="width: 70%;">
<img src="./media\grasp\grasp.jpg" alt="Pipeline Image">
</figure>
</div>
<div class="content has-text-justified">
<p>
In language-guided robotic grasping, existing methods rely on voxelization or multi-view inputs,
which can introduce approximation errors. Instead, our approach retrains GraspNet by directly using 3D Gasussians as input
, avoiding resampling and enabling faster speed
</p>
</div>
<!--/ Matting. -->
</div>
<style>
.before,
.after {
margin: 0;
}
.before figcaption,
.after figcaption {
background: #fff;
border: 1px solid #c0c0c0;
border-radius: 4px;
color: #2e3452;
opacity: 0.8;
padding: 4px;
position: absolute;
top: 50%;
transform: translateY(-50%);
line-height: 100%;
}
.before figcaption {
left: 4px;
}
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</style>
</div>
</section>
<section class="section">
<div class="container is-max-desktop">
<h3 class="title is-3"> Qualitative results </h3>
<div class="columns is-centered">
<figure class="image is-centered" style="width: 70%;">
<img src="./media\distill\compare.jpg" alt="Pipeline Image">
</figure>
</div>
<div class="content has-text-justified">
<p>
SparseGrasp is robotst under sparse-view setting, enbabling better semantic distillation effect.
</p>
</div>
<!--/ Matting. -->
</div>
<style>
.before,
.after {
margin: 0;
}
.before figcaption,
.after figcaption {
background: #fff;
border: 1px solid #c0c0c0;
border-radius: 4px;
color: #2e3452;
opacity: 0.8;
padding: 4px;
position: absolute;
top: 50%;
transform: translateY(-50%);
line-height: 100%;
}
.before figcaption {
left: 4px;
}
.after figcaption {
right: 4px;
}
</style>
</div>
</section>
<!-- <section class="section" id="BibTeX">
<div class="container is-max-desktop content">
<h2 class="title">BibTeX</h2>
<pre><code>@inproceedings{zhou2024feature,
title={Feature 3dgs: Supercharging 3d gaussian splatting to enable distilled feature fields},
author={Zhou, Shijie and Chang, Haoran and Jiang, Sicheng and Fan, Zhiwen and Zhu, Zehao and Xu, Dejia and Chari, Pradyumna and You, Suya and Wang, Zhangyang and Kadambi, Achuta},
booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
pages={21676--21685},
year={2024}
}</code></pre>
</div>
</section> -->
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