Kestrel: 3D Multimodal LLM for Part-Aware Grounded Description

• URL: http://arxiv.org/abs/2405.18937v2
• Date: Mon, 04 Aug 2025 13:54:40 GMT
• Title: Kestrel: 3D Multimodal LLM for Part-Aware Grounded Description
• Authors: Mahmoud Ahmed, Junjie Fei, Jian Ding, Eslam Mohamed Bakr, Mohamed Elhoseiny,
• Abstract summary: Part-Aware Point Grounded Description (PaPGD) is a challenging task aimed at advancing 3D multimodal learning for fine-grained, part-aware segmentation grounding.<n>We present the 3DCoMPaT Grounded Instructions (3DCoMPaT-GrIn) dataset, a comprehensive resource that pairs rich point cloud descriptions with corresponding part-level segmentation masks.<n>We propose Kestrel, a part-aware 3D multimodal large language model that integrates an advanced language model for nuanced language comprehension with multi-level point feature propagation and query refinement mechanism.
• Score: 33.55332803244455
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, we introduce Part-Aware Point Grounded Description (PaPGD), a challenging task aimed at advancing 3D multimodal learning for fine-grained, part-aware segmentation grounding and detailed explanation of 3D objects. Existing 3D datasets largely focus on either vision-only part segmentation or vision-language scene segmentation, lacking the fine-grained multimodal segmentation needed for robotic navigation and interaction in real-world environments. To address this gap, we present the 3DCoMPaT Grounded Instructions (3DCoMPaT-GrIn) Dataset, a comprehensive resource that pairs rich point cloud descriptions with corresponding part-level segmentation masks. This dataset encompasses extensive samples designed for both PaPGD and fine-grained single-part grounding tasks. To tackle the inherent challenges of grounding objects and generating grounded descriptions at the part level, we propose Kestrel, a part-aware 3D multimodal large language model that integrates an advanced language model for nuanced language comprehension with multi-level point feature propagation and query refinement mechanism to enhance spatial reasoning at the part level. The extensive experiments demonstrate that Kestrel effectively bridges the gap between part-aware language understanding and 3D segmentation grounding, paving the way for more robust and interpretable 3D object comprehension that meets the demands of real-world robotic applications. Project page at https://feielysia.github.io/Kestrel.github.io/

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