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<!DOCTYPE html>
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<title>InstanceGaussian: Appearance-Semantic Joint Gaussian Representation for 3D Instance-Level Perception
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<h1 class="title is-3 publication-title">InstanceGaussian: Appearance-Semantic Joint Gaussian Representation for 3D Instance-Level Perception</h1>
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<a href="FIRST AUTHOR PERSONAL LINK" target="_blank">Haijie Li</a></sup>,</span>
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<a href="SECOND AUTHOR PERSONAL LINK" target="_blank">Yanmin Wu</a>,</span>
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<a href="THIRD AUTHOR PERSONAL LINK" target="_blank">Jiarui Meng</a>,</span>
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<a href="Foutrh AUTHOR PERSONAL LINK" target="_blank">Qiankun Gao</a>,</span>
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<a href="Fifth AUTHOR PERSONAL LINK" target="_blank">Zhirao Zhang</a>,</span>
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<a href="Sixth AUTHOR PERSONAL LINK" target="_blank">Ronggang Wang</a>,</span>
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<a href="Seventh AUTHOR PERSONAL LINK" target="_blank">Jian Zhang</a>,</span>
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<span class="author-block">Peking University<br>
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<h2 class="title is-4">Framework</h2>
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<figcaption style="margin-top: 10px; font-size: 10px; color: gray;">
Top row: Appearance-semantic joint Gaussian representation avoids the imbalance and inconsistency in appearance-semantic learning. Bottom row: Bottom-up instantiation: Over-segmentation is achieved via FPS sampling and clustering, followed by instantiation through graph-connectivity-based aggregation.
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<h2 class="title is-4">Abstract</h2>
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<p style="font-size: 0.8rem;">
3D scene understanding has become an essential area of research with applications in autonomous driving, robotics, and augmented reality. Recently, 3D Gaussian Splatting (3DGS) has emerged as a powerful approach, combining explicit modeling with neural adaptability to provide efficient and detailed scene representations. However, three major challenges remain in leveraging 3DGS for scene understanding: 1) an imbalance between appearance and semantics, where dense Gaussian usage for fine-grained texture modeling does not align with the minimal requirements for semantic attributes; 2) inconsistencies between appearance and semantics, as purely appearance-based Gaussians often misrepresent object boundaries; and 3) reliance on top-down instance segmentation methods, which struggle with uneven category distributions, leading to over- or under-segmentation. In this work, we propose InstanceGaussian, a method that jointly learns appearance and semantic features while adaptively aggregating instances. Our contributions include i) a novel Semantic-Scaffold-GS representation balancing appearance and semantics to improve feature representations and boundary delineation; ii) a progressive appearance-semantic joint training strategy to enhance stability and segmentation accuracy; and iii) a bottom-up, category-agnostic instance aggregation approach that addresses segmentation challenges through farthest point sampling and connected component analysis. Our approach achieves state-of-the-art performance in category-agnostic, open-vocabulary 3D point-level segmentation, highlighting the effectiveness of the proposed representation and training strategies.
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Visualization comparison of category-agnostic 3D instance segmentation result.
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Open-vocabulary query point cloud Understanding on Scannet dataset.
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