IntuiTF: MLLM-Guided Transfer Function Optimization for Direct Volume Rendering
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arXiv
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| Autores principales: | , , , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866915485138812928 |
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| author | Wang, Yiyao Pan, Bo Wang, Ke Liu, Han Mao, Jinyuan Liu, Yuxin Zhu, Minfeng Huang, Xiuqi Chen, Weifeng Zhang, Bo Chen, Wei |
| author_facet | Wang, Yiyao Pan, Bo Wang, Ke Liu, Han Mao, Jinyuan Liu, Yuxin Zhu, Minfeng Huang, Xiuqi Chen, Weifeng Zhang, Bo Chen, Wei |
| contents | Direct volume rendering (DVR) is a fundamental technique for visualizing volumetric data, where transfer functions (TFs) play a crucial role in extracting meaningful structures. However, designing effective TFs remains unintuitive due to the semantic gap between user intent and TF parameter space. Although numerous TF optimization methods have been proposed to mitigate this issue, existing approaches still face two major challenges: the vast exploration space and limited generalizability. To address these issues, we propose IntuiTF, a novel framework that leverages Multimodal Large Language Models (MLLMs) to guide TF optimization in alignment with user intent. Specifically, our method consists of two key components: (1) an evolution-driven explorer for effective exploration of the TF space, and (2) an MLLM-guided human-aligned evaluator that provides generalizable visual feedback on rendering quality. The explorer and the evaluator together establish an efficient Trial-Insight-Replanning paradigm for TF space exploration. We further extend our framework with an interactive TF design system. We demonstrate the broad applicability of our framework through three case studies and validate the effectiveness of each component through extensive experiments. We strongly recommend readers check our cases, demo video, and source code at: https://github.com/wyysteelhead/IntuiTF |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_18407 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | IntuiTF: MLLM-Guided Transfer Function Optimization for Direct Volume Rendering Wang, Yiyao Pan, Bo Wang, Ke Liu, Han Mao, Jinyuan Liu, Yuxin Zhu, Minfeng Huang, Xiuqi Chen, Weifeng Zhang, Bo Chen, Wei Graphics Computer Vision and Pattern Recognition Direct volume rendering (DVR) is a fundamental technique for visualizing volumetric data, where transfer functions (TFs) play a crucial role in extracting meaningful structures. However, designing effective TFs remains unintuitive due to the semantic gap between user intent and TF parameter space. Although numerous TF optimization methods have been proposed to mitigate this issue, existing approaches still face two major challenges: the vast exploration space and limited generalizability. To address these issues, we propose IntuiTF, a novel framework that leverages Multimodal Large Language Models (MLLMs) to guide TF optimization in alignment with user intent. Specifically, our method consists of two key components: (1) an evolution-driven explorer for effective exploration of the TF space, and (2) an MLLM-guided human-aligned evaluator that provides generalizable visual feedback on rendering quality. The explorer and the evaluator together establish an efficient Trial-Insight-Replanning paradigm for TF space exploration. We further extend our framework with an interactive TF design system. We demonstrate the broad applicability of our framework through three case studies and validate the effectiveness of each component through extensive experiments. We strongly recommend readers check our cases, demo video, and source code at: https://github.com/wyysteelhead/IntuiTF |
| title | IntuiTF: MLLM-Guided Transfer Function Optimization for Direct Volume Rendering |
| topic | Graphics Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2506.18407 |