ObjectGS: Object-aware Scene Reconstruction and Scene Understanding via Gaussian Splatting
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arXiv
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| Autori principali: | , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866915402542481408 |
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| author | Zhu, Ruijie Yu, Mulin Xu, Linning Jiang, Lihan Li, Yixuan Zhang, Tianzhu Pang, Jiangmiao Dai, Bo |
| author_facet | Zhu, Ruijie Yu, Mulin Xu, Linning Jiang, Lihan Li, Yixuan Zhang, Tianzhu Pang, Jiangmiao Dai, Bo |
| contents | 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 |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_15454 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | ObjectGS: Object-aware Scene Reconstruction and Scene Understanding via Gaussian Splatting Zhu, Ruijie Yu, Mulin Xu, Linning Jiang, Lihan Li, Yixuan Zhang, Tianzhu Pang, Jiangmiao Dai, Bo Graphics Artificial Intelligence Computer Vision and Pattern Recognition Human-Computer Interaction 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 |
| title | ObjectGS: Object-aware Scene Reconstruction and Scene Understanding via Gaussian Splatting |
| topic | Graphics Artificial Intelligence Computer Vision and Pattern Recognition Human-Computer Interaction |
| url | https://arxiv.org/abs/2507.15454 |