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DyGASR: Dynamic Generalized Gaussian Splatting with Surface Alignment for Accelerated 3D Mesh Reconstruction

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Pattern Recognition and Computer Vision (PRCV 2024)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 15036))

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Abstract

Recent advancements in 3D Gaussian Splatting (3DGS), which lead to high-quality novel view synthesis and accelerated rendering, have remarkably improved the quality of radiance field reconstruction. However, the extraction of mesh from a massive number of minute 3D Gaussian points remains great challenge due to the large volume of Gaussians and difficulty of representation of sharp signals caused by their inherent low-pass characteristics. To address this issue, we propose DyGASR, which utilizes generalized Gaussian instead of traditional 3D Gaussian to decrease the number of particles and dynamically optimize the representation of the captured signal. In addition, it is observed that reconstructing mesh with generalized Gaussian splatting without modifications frequently leads to failures since the Gaussian centroids may not precisely align with the scene surface. To overcome this, we further introduce a Generalized Surface Regularization (GSR), which reduces the smallest scaling vector of each Gaussian to zero and ensures normal alignment perpendicular to the surface, facilitating subsequent Poisson surface mesh reconstruction. Additionally, we propose a dynamic resolution adjustment strategy that utilizes a cosine schedule to gradually increase image resolution from low to high during the training stage, thus avoiding constant full resolution, which significantly boosts the reconstruction speed. Our approach surpasses existing 3DGS-based mesh reconstruction methods, as evidenced by extensive evaluations on various scene datasets, demonstrating a 25% increase in speed, and a 30% reduction in memory usage.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China under Grant 62071006.

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Authors and Affiliations

  1. School of Information Science and Technology, North China University of Technology, Beijing, China

    Shengchao Zhao & Yundong Li

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  1. Shengchao Zhao
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  2. Yundong Li
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Correspondence to Yundong Li .

Editor information

Editors and Affiliations

  1. Peking University, Beijing, China

    Zhouchen Lin

  2. Nankai University, Tianjin, China

    Ming-Ming Cheng

  3. Chinese Academy of Sciences, Beijing, China

    Ran He

  4. Xinjiang University, Ürümqi, Xinjiang, China

    Kurban Ubul

  5. Xinjiang University, Ürümqi, China

    Wushouer Silamu

  6. Peking University, Beijing, China

    Hongbin Zha

  7. Tsinghua University, Beijing, China

    Jie Zhou

  8. Chinese Academy of Sciences, Beijing, China

    Cheng-Lin Liu

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Zhao, S., Li, Y. (2025). DyGASR: Dynamic Generalized Gaussian Splatting with Surface Alignment for Accelerated 3D Mesh Reconstruction. In: Lin, Z., et al. Pattern Recognition and Computer Vision. PRCV 2024. Lecture Notes in Computer Science, vol 15036. Springer, Singapore. https://doi.org/10.1007/978-981-97-8508-7_21

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  • DOI: https://doi.org/10.1007/978-981-97-8508-7_21

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  • Online ISBN: 978-981-97-8508-7

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