Search3D: Hierarchical Open-Vocabulary 3D Segmentation

• URL: http://arxiv.org/abs/2409.18431v2
• Date: Wed, 22 Jan 2025 15:09:00 GMT
• Title: Search3D: Hierarchical Open-Vocabulary 3D Segmentation
• Authors: Ayca Takmaz, Alexandros Delitzas, Robert W. Sumner, Francis Engelmann, Johanna Wald, Federico Tombari,
• Abstract summary: We introduce Search3D, an approach to construct hierarchical open-vocabulary 3D scene representations. Unlike prior methods, Search3D shifts towards a more flexible open-vocabulary 3D search paradigm. For systematic evaluation, we contribute a scene-scale open-vocabulary 3D part segmentation benchmark based on MultiScan.
• Score: 78.47704793095669
• License:
• Abstract: 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.

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