Kavli Affiliate: Ke Wang
| First 5 Authors: Yiyao Wang, Yiyao Wang, , ,
| Summary:
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
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