Natural Language-Driven Viewpoint Navigation for Volume Exploration via Semantic Block Representation

2025-08-09

AI Summary

Non-expert users struggle to identify effective 3D viewpoints for volumetric data exploration due to limited domain knowledge and spatial reasoning skills. Method: We propose a natural language–driven, semantic-aware navigation framework that partitions volumetric data into semantic blocks, encodes their structural features, and computes CLIP Score—using the CLIP model—to measure semantic alignment between user-provided text queries and rendered images of candidate viewpoints. This score serves as the reward signal in a reinforcement learning pipeline to automatically search for optimal viewpoints. Contribution/Results: To our knowledge, this is the first work to integrate vision-language alignment (via CLIP Score) into volumetric visualization navigation, enabling end-to-end semantic mapping from user intent to geometric viewpoint. Experiments demonstrate significant improvements in viewpoint recommendation accuracy and interpretability, substantially lowering the barrier to 3D navigation and facilitating intuitive understanding of complex scientific phenomena by non-experts.

Technology Category

• Computer Vision: Language and Vision
• Natural Language Processing: Sentence-level Semantics, Textual Inference, etc.
• Search and Optimization: Learning to Search

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

Exploring volumetric data is crucial for interpreting scientific datasets. However, selecting optimal viewpoints for effective navigation can be challenging, particularly for users without extensive domain expertise or familiarity with 3D navigation. In this paper, we propose a novel framework that leverages natural language interaction to enhance volumetric data exploration. Our approach encodes volumetric blocks to capture and differentiate underlying structures. It further incorporates a CLIP Score mechanism, which provides semantic information to the blocks to guide navigation. The navigation is empowered by a reinforcement learning framework that leverage these semantic cues to efficiently search for and identify desired viewpoints that align with the user's intent. The selected viewpoints are evaluated using CLIP Score to ensure that they best reflect the user queries. By automating viewpoint selection, our method improves the efficiency of volumetric data navigation and enhances the interpretability of complex scientific phenomena.

Problem

• Enables natural language-driven viewpoint navigation for volumetric data
• Automates optimal viewpoint selection using reinforcement learning
• Enhances interpretability of complex scientific datasets via semantic blocks

Innovation

• Natural language interaction for viewpoint navigation
• Semantic block representation with CLIP Score
• Reinforcement learning for efficient viewpoint search