Search3D: Hierarchical Open-Vocabulary 3D Segmentation
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866929683780599808 |
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| author | Takmaz, Ayca Delitzas, Alexandros Sumner, Robert W. Engelmann, Francis Wald, Johanna Tombari, Federico |
| author_facet | Takmaz, Ayca Delitzas, Alexandros Sumner, Robert W. Engelmann, Francis Wald, Johanna Tombari, Federico |
| contents | Open-vocabulary 3D segmentation enables exploration of 3D spaces using free-form text descriptions. Existing methods for open-vocabulary 3D instance segmentation primarily focus on identifying object-level instances but struggle with finer-grained scene entities such as object parts, or regions described by generic attributes. In this work, we introduce Search3D, an approach to construct hierarchical open-vocabulary 3D scene representations, enabling 3D search at multiple levels of granularity: fine-grained object parts, entire objects, or regions described by attributes like materials. Unlike prior methods, Search3D shifts towards a more flexible open-vocabulary 3D search paradigm, moving beyond explicit object-centric queries. For systematic evaluation, we further contribute a scene-scale open-vocabulary 3D part segmentation benchmark based on MultiScan, along with a set of open-vocabulary fine-grained part annotations on ScanNet++. Search3D outperforms baselines in scene-scale open-vocabulary 3D part segmentation, while maintaining strong performance in segmenting 3D objects and materials. Our project page is http://search3d-segmentation.github.io. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2409_18431 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Search3D: Hierarchical Open-Vocabulary 3D Segmentation Takmaz, Ayca Delitzas, Alexandros Sumner, Robert W. Engelmann, Francis Wald, Johanna Tombari, Federico Computer Vision and Pattern Recognition Open-vocabulary 3D segmentation enables exploration of 3D spaces using free-form text descriptions. Existing methods for open-vocabulary 3D instance segmentation primarily focus on identifying object-level instances but struggle with finer-grained scene entities such as object parts, or regions described by generic attributes. In this work, we introduce Search3D, an approach to construct hierarchical open-vocabulary 3D scene representations, enabling 3D search at multiple levels of granularity: fine-grained object parts, entire objects, or regions described by attributes like materials. Unlike prior methods, Search3D shifts towards a more flexible open-vocabulary 3D search paradigm, moving beyond explicit object-centric queries. For systematic evaluation, we further contribute a scene-scale open-vocabulary 3D part segmentation benchmark based on MultiScan, along with a set of open-vocabulary fine-grained part annotations on ScanNet++. Search3D outperforms baselines in scene-scale open-vocabulary 3D part segmentation, while maintaining strong performance in segmenting 3D objects and materials. Our project page is http://search3d-segmentation.github.io. |
| title | Search3D: Hierarchical Open-Vocabulary 3D Segmentation |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2409.18431 |