ObjectGS: Object-aware Scene Reconstruction and Scene Understanding via Gaussian Splatting

• URL: http://arxiv.org/abs/2507.15454v1
• Date: Mon, 21 Jul 2025 10:06:23 GMT
• Title: ObjectGS: Object-aware Scene Reconstruction and Scene Understanding via Gaussian Splatting
• Authors: Ruijie Zhu, Mulin Yu, Linning Xu, Lihan Jiang, Yixuan Li, Tianzhu Zhang, Jiangmiao Pang, Bo Dai,
• Abstract summary: ObjectGS is an object-aware framework that unifies 3D scene reconstruction with semantic understanding.<n>We show through experiments that ObjectGS outperforms state-of-the-art methods on open-vocabulary and panoptic segmentation tasks.
• Score: 54.92763171355442
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 3D Gaussian Splatting is renowned for its high-fidelity reconstructions and real-time novel view synthesis, yet its lack of semantic understanding limits object-level perception. In this work, we propose ObjectGS, an object-aware framework that unifies 3D scene reconstruction with semantic understanding. Instead of treating the scene as a unified whole, ObjectGS models individual objects as local anchors that generate neural Gaussians and share object IDs, enabling precise object-level reconstruction. During training, we dynamically grow or prune these anchors and optimize their features, while a one-hot ID encoding with a classification loss enforces clear semantic constraints. We show through extensive experiments that ObjectGS not only outperforms state-of-the-art methods on open-vocabulary and panoptic segmentation tasks, but also integrates seamlessly with applications like mesh extraction and scene editing. Project page: https://ruijiezhu94.github.io/ObjectGS_page

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