Paper Detail
Shaohui Dai, Yansong Qu, You Shen, Shengchuan Zhang, Liujuan Cao
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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@article{dai2026par3d,
title = {PAR3D: A Unified 3D-MLLM with Part-Aware Representation for Scene Understanding},
author = {Shaohui Dai and Yansong Qu and You Shen and Shengchuan Zhang and Liujuan Cao},
year = {2026},
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, r},
url = {https://arxiv.org/abs/2606.06485},
keywords = {cs.CV},
eprint = {2606.06485},
archiveprefix = {arXiv},
}
{}