Disc3D: Automatic Curation of High-Quality 3D Dialog Data via Discriminative Object Referring
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866918296431886336 |
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| author | Wei, Siyuan Wang, Chunjie Liu, Xiao Yan, Xiaosheng Zhou, Zhishan Huang, Rui |
| author_facet | Wei, Siyuan Wang, Chunjie Liu, Xiao Yan, Xiaosheng Zhou, Zhishan Huang, Rui |
| contents | 3D Multi-modal Large Language Models (MLLMs) still lag behind their 2D peers, largely because large-scale, high-quality 3D scene-dialogue datasets remain scarce. Prior efforts hinge on expensive human annotation and leave two key ambiguities unresolved: viewpoint ambiguity, where spatial language presumes unknown camera poses, and object referring ambiguity, where non-exclusive descriptions blur the line between targets and distractors. We therefore present a fully automated pipeline that converts raw 3D scans into unambiguous, high-quality dialogue data at a fraction of the previous cost. By synergizing rule-based constraints with 2D MLLMs and LLMs, the pipeline enables controllable, scalable generation without human intervention. The pipeline comprises four stages: (1) meta-annotation collection harvesting object-, frame-, and scene-level captions, (2) scene graph construction with relation correction to capture proximal object relations, (3) discriminative object referring that generates exclusive and compact descriptions, and (4) multi-task data generation synthesizing diverse dialogues. Our pipeline systematically mitigates inherent flaws in source datasets and produces the final Disc3D dataset, over 2 million samples in 25K hybrid 3D scenes, spanning scene, view, and object captioning, visual grounding, and five object-centric QA tasks. Extensive experiments demonstrate that training with Disc3D yields consistent, significant improvements on both public benchmarks and our multifaceted Disc3D-QA tasks. Code, data, and models will be publicly available. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_18817 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Disc3D: Automatic Curation of High-Quality 3D Dialog Data via Discriminative Object Referring Wei, Siyuan Wang, Chunjie Liu, Xiao Yan, Xiaosheng Zhou, Zhishan Huang, Rui Computer Vision and Pattern Recognition 3D Multi-modal Large Language Models (MLLMs) still lag behind their 2D peers, largely because large-scale, high-quality 3D scene-dialogue datasets remain scarce. Prior efforts hinge on expensive human annotation and leave two key ambiguities unresolved: viewpoint ambiguity, where spatial language presumes unknown camera poses, and object referring ambiguity, where non-exclusive descriptions blur the line between targets and distractors. We therefore present a fully automated pipeline that converts raw 3D scans into unambiguous, high-quality dialogue data at a fraction of the previous cost. By synergizing rule-based constraints with 2D MLLMs and LLMs, the pipeline enables controllable, scalable generation without human intervention. The pipeline comprises four stages: (1) meta-annotation collection harvesting object-, frame-, and scene-level captions, (2) scene graph construction with relation correction to capture proximal object relations, (3) discriminative object referring that generates exclusive and compact descriptions, and (4) multi-task data generation synthesizing diverse dialogues. Our pipeline systematically mitigates inherent flaws in source datasets and produces the final Disc3D dataset, over 2 million samples in 25K hybrid 3D scenes, spanning scene, view, and object captioning, visual grounding, and five object-centric QA tasks. Extensive experiments demonstrate that training with Disc3D yields consistent, significant improvements on both public benchmarks and our multifaceted Disc3D-QA tasks. Code, data, and models will be publicly available. |
| title | Disc3D: Automatic Curation of High-Quality 3D Dialog Data via Discriminative Object Referring |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2511.18817 |