PAR3D: A Unified 3D-MLLM with Part-Aware Representation for Scene Understanding

Shaohui Dai; Yansong Qu; You Shen; Shengchuan Zhang; Liujuan Cao

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arXiv · 2606.06485

PAR3D: A Unified 3D-MLLM with Part-Aware Representation for Scene Understanding

Shaohui Dai·Yansong Qu·You Shen·Shengchuan Zhang·Liujuan Cao

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Abstract

Recent advances in 3D multimodal large language models (3D-MLLMs) have enabled unified solutions for 3D scene understanding tasks, including visual question answering, captioning, and referring segmentation. However, existing 3D-MLLMs remain largely object-centric, limiting their ability to model fine-grained part structures that are essential for embodied interaction with 3D environments. In this work, we present PAR3D, a unified part-aware 3D-MLLM framework that enables models to understand, reason about, and ground both objects and their parts in 3D scenes. To enable training and evaluation of part-aware 3D scene understanding, we introduce ScenePart, a synthetic 3D scene dataset with part-level annotations and language instructions. We further develop Part-Aware 3D Representation Learning to enrich 3D visual representations with fine-grained part-level semantics, and propose Hierarchical Segmentation Query Generation to ground part targets via hierarchical object-part queries. Extensive experiments show that our method substantially improves part-level question answering and referring segmentation, while also achieving strong performance across object-level vision-language tasks.

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Shaohui Dai, Yansong Qu, You Shen, Shengchuan Zhang, Liujuan Cao. 2026-06-04. PAR3D: A Unified 3D-MLLM with Part-Aware Representation for Scene Understanding. https://arxiv.org/abs/2606.06485

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