PAR3D: A Unified 3D-MLLM with Part-Aware Representation for Scene Understanding
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 ...
| Authors | Shaohui Dai, Yansong Qu, You Shen et al. |
|---|---|
| Primary category | cs.CV |
| Published | 4 June 2026 |
| Last updated at source | 4 June 2026 |
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