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24<title>Guo Chen</title><meta name="description" content="Master student at Peking University."/><meta name="author" content="Guo Chen"/><meta name="keywords" content="Guo Chen,PhD,Research,Peking University"/><meta name="creator" content="Guo Chen"/><meta name="publisher" content="Guo Chen"/><meta property="og:title" content="Guo Chen"/><meta property="og:description" content="Master student at Peking University."/><meta property="og:site_name" content="Guo Chen&#x27;s Academic Website"/><meta property="og:locale" content="en_US"/><meta property="og:type" content="website"/><meta name="twitter:card" content="summary"/><meta name="twitter:title" content="Guo Chen"/><meta name="twitter:description" content="Master student at Peking University."/><link rel="icon" href="/favicon.svg"/>
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24k:text-neutral-400 hover:text-accent transition-colors duration-200" aria-label="GitHub"><svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="lucide lucide-github h-5 w-5" aria-hidden="true"><path d="M15 22v-4a4.8 4.8 0 0 0-1-3.5c3 0 6-2 6-5.5.08-1.25-.27-2.48-1-3.5.28-1.15.28-2.35 0-3.5 0 0-1 0-3 1.5-2.64-.5-5.36-.5-8 0C6 2 5 2 5 2c-.3 1.15-.3 2.35 0 3.5A5.403 5.403 0 0 0 4 9c0 3.5 3 5.5 6 5.5-.39.49-.68 1.05-.85 1.65-.17.6-.22 1.23-.15 1.85v4"></path><path d="M9 18c-4.51 2-5-2-7-2"></path></svg></a></div><div class="bg-neutral-100 dark:bg-neutral-800 rounded-lg p-4 mb-6 hover:shadow-lg transition-all duration-200 hover:scale-[1.02]"><h3 class="font-semibold text-primary mb-3">Research Interests</h3><div class="space-y-2 text-sm text-neutral-700 dark:text-neutral-500"><div>Computer Vision</div><div>Computer Graphics</div></div></div><div class="bg-neutral-100 dark:bg-neutral-800 rounded-lg p-4 mb-6 hover:shadow-lg transition-all duration-200 hover:scale-[1.02]"><h3 class="font-semibold text-primary mb-3">🌍 Visitor Map</h3>
24<p class="text-xs text-neutral-600 dark:text-neutral-500 mb-3 text-center">Thanks for visiting! 🌍</p><div class="flex justify-center items-center"><a href="https://mapmyvisitors.com/web/1c16c" title="Visit tracker"><img src="https://mapmyvisitors.com/map.png?cl=ffffff&amp;w=350&amp;t=tt&amp;d=kfgBcuX84xjQcMCjjy2yC_5xoaVLK4fSABEJfns10Ns"/></a></div></div><div class="flex justify-center"><div class="relative"><button class="flex items-center space-x-2 px-4 py-2 rounded-lg font-medium text-sm transition-all duration-200 bg-neutral-100 dark:bg-neutral-800 text-neutral-700 dark:text-neutral-500 hover:bg-red-50 dark:hover:bg-red-900/20 hover:text-red-600 dark:hover:text-red-400 cursor-pointer" tabindex="0"><svg xmlns="http://www.w3.org/2000/svg" fill="none" viewBox="0 0 24 24" stroke-width="1.5" stroke="currentColor" aria-hidden="true" data-slot="icon" class="h-4 w-4"><path stroke-linecap="round" stroke-linejoin="round" d="M21 8.25c0-2.485-2.099-4.5-4.688-4.5-1.935 0-3.597 1.126-4.312 2.733-.715-1.607-2.377-2.733-4.313-2.733C5.1 3.75 3 5.765 3 8.25c0 7.22 9 12 9 12s9-4.78 9-12Z"></path></svg><span>Like</span></button></div></div></div></div><div class="lg:col-span-2 space-y-8"><section id="about" class="scroll-mt-24 space-y-8"><section style="opacity:0;transform:translateY(20px)"><h2 class="text-2xl font-serif font-bold text-primary mb-4">About</h2><div class="text-neutral-700 dark:text-neutral-600 leading-relaxed"><ul class="list-disc list-inside mb-4 space-y-1 ml-4">
25<li class="mb-1">I&#x27;m Guo Chen, a 3DV and Graphics Lover.</li>
26<li class="mb-1">I am a first-year master’s student at the School of Mathematical Sciences (<a href="http://english.math.pku.edu.cn/" node="[object Object]" target="_blank" rel="noopener noreferrer" class="text-accent font-medium transition-all duration-200 rounded hover:bg-accent/10 hover:shadow-sm">SMS</a>), Peking University, advised by Prof. <a href="http://www.muyadong.com/" node="[object Object]" target="_blank" rel="noopener noreferrer" class="text-accent font-medium transition-all duration-200 rounded hover:bg-accent/10 hover:shadow-sm">Yadong Mu</a>.</li>
27<li class="mb-1">I obtained my B.S. from Beijing Normal University, advised by Prof. <a href="https://shishenghuang.github.io/index/" node="[object Object]" target="_blank" rel="noopener noreferrer" class="text-accent font-medium transition-all duration-200 rounded hover:bg-accent/10 hover:shadow-sm">Shisheng Huang</a> and <a href="https://ai.bnu.edu.cn/xygk/szdw/zgj/194482e0996d4044806ac39019896e9c.htm" node="[object Object]" target="_blank" rel="noopener noreferrer" class="text-accent font-medium transition-all duration-200 rounded hover:bg-accent/10 hover:shadow-sm">Hua Huang</a>. I used to be a member of <a href="https://vmcl.bnu.edu.cn/index.html" node="[object Object]" target="_blank" rel="noopener noreferrer" class="text-accent font-medium transition-all duration-200 rounded hover:bg-accent/10 hover:shadow-sm">IMCLab</a> in BNU.</li>
28</ul>
29<p class="mb-4 last:mb-0">My research interests lie in 3D computer vision, with a focus on reconstructing and editing the 3D world and interactive 3D content for robotics and immersive applications.</p>
30<blockquote class="border-l-4 border-accent/50 pl-4 italic my-4 text-neutral-600 dar
30k:text-neutral-500">
31<p class="mb-4 last:mb-0"><em class="italic text-neutral-600 dark:text-neutral-500"><strong class="font-semibold text-primary">I am actively looking for PhD opportunities starting in Fall 2027!</strong></em></p>
32</blockquote></div></section><section style="opacity:0;transform:translateY(20px)"><div class="flex items-center justify-between mb-4"><h2 class="text-2xl font-serif font-bold text-primary">Selected Publications</h2><a class="text-accent hover:text-accent-dark text-sm font-medium transition-all duration-200 rounded hover:bg-accent/10 hover:shadow-sm" href="/#publications">View All →</a></div><div class="space-y-4"><div class="bg-neutral-50 dark:bg-neutral-800 p-4 rounded-lg shadow-sm border border-neutral-200 dark:border-[rgba(148,163,184,0.24)] hover:shadow-lg transition-all duration-200 hover:scale-[1.02]" style="opacity:0;transform:translateY(20px)"><h3 class="font-semibold text-primary mb-2 leading-tight">GS-RoadPatching: Inpainting Gaussians via 3D Searching and Placing for Driving Scenes</h3>
32<p class="text-sm text-neutral-600 dark:text-neutral-500 mb-1"><span><span class="">Guo Chen</span>, </span><span><span class="">Jiarun Liu</span>, </span><span><span class="">Sicong Du</span>, </span><span><span class="">Chenming Wu</span>, </span><span><span class="">Deqi Li</span>, </span><span><span class="">Shi-Sheng Huang</span>, </span><span><span class="">Guofeng Zhang</span>, </span><span><span class="">Sheng Yang</span></span></p><p class="text-sm text-neutral-600 dark:text-neutral-500 mb-2">Proceedings of the SIGGRAPH Asia 2025 Conference Papers</p></div><div class="bg-neutral-50 dark:bg-neutral-800 p-4 rounded-lg shadow-sm border border-neutral-200 dark:border-[rgba(148,163,184,0.24)] hover:shadow-lg transition-all duration-200 hover:scale-[1.02]" style="opacity:0;transform:translateY(20px)"><h3 class="font-semibold text-primary mb-2 leading-tight">NeuralIndicator: implicit surface reconstruction from neural indicator priors</h3><p class="text-sm text-neutral-600 dark:text-neutral-500 mb-1"><span><span class="">Shi-Sheng Huang</span>, </span><span><span class="">Guo Chen</span>, </span><span><span class="">Li Heng Chen</span>, </span><span><span class="">Hua Huang</span></span></p>
32<p class="text-sm text-neutral-600 dark:text-neutral-500 mb-2">Proceedings of the 41st International Conference on Machine Learning</p></div></div></section><section style="opacity:0;transform:translateY(20px)"><h2 class="text-2xl font-serif font-bold text-primary mb-4">News</h2><div class="space-y-3"><div class="flex items-start space-x-3"><span class="text-xs text-neutral-500 mt-1 w-16 flex-shrink-0">2025-09</span><p class="text-sm text-neutral-700">One paper GS-RoadPatching is conditionally accepted by SIGGRAPH Asia 2025 (conference paper) 🎉</p></div><div class="flex items-start space-x-3"><span class="text-xs text-neutral-500 mt-1 w-16 flex-shrink-0">2025-07</span><p class="text-sm text-neutral-700">I graduated from Beijing Normal University! 🎓🎉</p></div><div class="flex items-start space-x-3"><span class="text-xs text-neutral-500 mt-1 w-16 flex-shrink-0">2025-06</span><p class="text-sm text-neutral-700">I received the &quot;Outstanding Graduate of Beijing&quot; award</p></div><div class="flex items-start space-x-3"><span class="text-xs text-neutral-500 mt-1 w-16 flex-shrink-0">2024-05</span><p class="text-sm text-neutral-700">One paper is accepted by ICML 2024.</p></div></div></section></section></div></div></div><!--$--><!--/$--><!--$--><!--/$--></main><footer class="border-t border-neutral-200/50 bg-neutral-50/50 dark:bg-neutral-900/50 dark:border-neutral-700/50"><div class="max-w-6xl mx-auto px-4 sm:px-6 lg:px-8 py-6"><div class="flex flex-col sm:flex-row justify-between items-center gap-2"><p class="text-xs text-neutral-500">Last updated: <!-- -->November 18, 2025</p><p class="text-xs text-neutral-500 flex items-center"><a href="https://github.com/xyjoey/PRISM" target="_blank" rel="noopener noreferrer">Built with PRISM</a><span class="ml-2">🚀</span></p></div></div></footer></div>
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32<script>self.__next_f.push([1,"ructed regions, which are represented by 3D Gaussian Splatting (3DGS). Unlike existing 3DGS inpainting methods that perform generative completion relying on 2D perspective-view-based diffusion or GAN models to predict limited appearance or depth cues for missing regions, our approach enables substitutional scene inpainting and editing directly through the 3DGS modality, extricating it from requiring spatial-temporal consistency of 2D cross-modals and eliminating the need for time-intensive retraining of Gaussians. Our key insight is that the highly repetitive patterns in driving scenes often share multi-modal similarities within the implicit 3DGS feature space and are particularly suitable for structural matching to enable effective 3DGS-based substitutional inpainting. Practically, we construct feature-embedded 3DGS scenes to incorporate a patch measurement method for abstracting local context at different scales and, subsequently, propose a structural search method to find candidate patches in 3D space effectively. Finally, we propose a simple yet effective substitution-and-fusion optimization for better visual harmony. We conduct extensive experiments on multiple publicly available datasets to demonstrate the effectiveness and efficiency of our proposed method in driving scenes, and the results validate that our method achieves state-of-the-art performance compared to the baseline methods in terms of both quality and interoperability. Additional experiments in general scenes also demonstrate the applicability of the proposed 3D inpainting strategy. The project page and code are available at: https://shanzhaguoo.github.io/GS-RoadPatching/.b:T9b6,"])</script>
32<script>self.__next_f.push([1,"@inproceedings{10.1145/3757377.3763892,\n  author = {Chen, Guo and Liu, Jiarun and Du, Sicong and Wu, Chenming and Li, Deqi and Huang, Shi-Sheng and Zhang, Guofeng and Yang, Sheng},\n  title = {GS-RoadPatching: Inpainting Gaussians via 3D Searching and Placing for Driving Scenes},\n  year = {2025},\n  isbn = {9798400721373},\n  publisher = {Association for Computing Machinery},\n  address = {New York, NY, USA},\n  url = {https://doi.org/10.1145/3757377.3763892},\n  doi = {10.1145/3757377.3763892},\n  abstract = {This paper presents GS-RoadPatching, an inpainting method for driving scene completion by referring to completely reconstructed regions, which are represented by 3D Gaussian Splatting (3DGS). Unlike existing 3DGS inpainting methods that perform generative completion relying on 2D perspective-view-based diffusion or GAN models to predict limited appearance or depth cues for missing regions, our approach enables substitutional scene inpainting and editing directly through the 3DGS modality, extricating it from requiring spatial-temporal consistency of 2D cross-modals and eliminating the need for time-intensive retraining of Gaussians. Our key insight is that the highly repetitive patterns in driving scenes often share multi-modal similarities within the implicit 3DGS feature space and are particularly suitable for structural matching to enable effective 3DGS-based substitutional inpainting. Practically, we construct feature-embedded 3DGS scenes to incorporate a patch measurement method for abstracting local context at different scales and, subsequently, propose a structural search method to find candidate patches in 3D space effectively. Finally, we propose a simple yet effective substitution-and-fusion optimization for better visual harmony. We conduct extensive experiments on multiple publicly available datasets to demonstrate the effectiveness and efficiency of our proposed method in driving scenes, and the results validate that our method achieves state-of-the-art performance compared to the baseline methods in terms of both quality and interoperability. Additional experiments in general scenes also demonstrate the applicability of the proposed 3D inpainting strategy. The project page and code are available at: https://shanzhaguoo.github.io/GS-RoadPatching/.},\n  booktitle = {Proceedings of the SIGGRAPH Asia 2025 Conference Papers},\n  articleno = {123},\n  numpages = {11},\n  location = {HongKong, China},\n  series = {SA Conference Papers '25}\n}"])</script>
32<script>self.__next_f.push([1,"c:T4e5,The neural implicit surface reconstruction from unorganized points is still challenging, especially when the point clouds are incomplete and/or noisy with complex topology structure. Unlike previous approaches performing neural implicit surface learning relying on local shape priors, this paper proposes to utilize global shape priors to regularize the neural implicit function learning for more reliable surface reconstruction. To this end, we first introduce a differentiable module to generate a smooth indicator function, which globally encodes both the indicative prior and local SDFs of the entire input point cloud. Benefit from this, we propose a new framework, called NeuralIndicator, to jointly learn both the smooth indicator function and neural implicit function simultaneously, using the global shape prior encoded by smooth indicator function to effectively regularize the neural implicit function learning, towards reliable and high-fidelity surface reconstruction from unorganized points without any normal information. Extensive evaluations on synthetic and real-scan datasets show that our approach consistently outperforms previous approaches, especially when point clouds are incomplete and/or noisy with complex topology structure.d:T6a6,@inproceedings{10.5555/3692070.3692861,\n  author = {Huang, Shi-Sheng and Chen, Guo and Chen, Li Heng and Huang, Hua},\n  title = {NeuralIndicator: implicit surface reconstruction from neural indicator priors},\n  year = {2024},\n  publisher = {JMLR.org},\n  abstract = {The neural implicit surface reconstruction from unorganized points is still challenging, especially when the point clouds are incomplete and/or noisy with complex topology structure. Unlike previous approaches performing neural implicit surface learning relying on local shape priors, this paper proposes to utilize global shape priors to regularize the neural implicit function learning for more reliable surface reconstruction. To this end, we first introduce a differentiable module to generate a smooth indicato"])</script>
32<script>self.__next_f.push([1,"r function, which globally encodes both the indicative prior and local SDFs of the entire input point cloud. Benefit from this, we propose a new framework, called NeuralIndicator, to jointly learn both the smooth indicator function and neural implicit function simultaneously, using the global shape prior encoded by smooth indicator function to effectively regularize the neural implicit function learning, towards reliable and high-fidelity surface reconstruction from unorganized points without any normal information. Extensive evaluations on synthetic and real-scan datasets show that our approach consistently outperforms previous approaches, especially when point clouds are incomplete and/or noisy with complex topology structure.},\n  booktitle = {Proceedings of the 41st International Conference on Machine Learning},\n  articleno = {791},\n  numpages = {13},\n  location = {Vienna, Austria},\n  series = {ICML'24}\n}"])</script>
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