首个面向物理实验的场景图数据集，支持科学视觉推理。
PhysScene: A Scene Graph Dataset for Scientific Visual Reasoning in Physics Experiments
- 构建物理实验专用场景图，包含仪器与功能关系
- 高语义约束与关系密度，挑战现有解析算法
- 适合研究科学场景理解与智能监控的学者
场景图（SG）通过建模物体及其成对关系，提供视觉场景的结构化表示。尽管近期取得进展，现有数据集主要聚焦通用自然场景，缺乏对领域特定与功能导向场景的探索。这一局限限制了科学实验场景中关系推理的评估，阻碍了智能监测与分析等应用的发展。为此，我们提出PhysScene，首个专为物理实验设计的场景图数据集。该数据集涵盖专用仪器、结构化实验装置及实验环境中的固有功能关系，支持超越空间共现的逻辑依赖推理。不同于追求大规模数据，PhysScene强调强语义约束与高关系密度，为现有场景解析算法带来新挑战，也提供了改进契机。大量分析与实验证明，PhysScene补充了现有基准，建立了推进科学视觉推理的宝贵测试平台。数据集已公开于 https://github.com/ZMH-SDUST/PhysScene。
原文摘要 · Abstract (English)
Scene Graphs (SGs) provide structured representations of visual scenes by modeling objects and their pairwise relationships. Despite recent progress, existing datasets primarily focus on generic natural contexts, leaving domain-specific and function-oriented scenes largely underexplored. This limitation restricts the evaluation of relational reasoning in scientific experimental scenes, thereby hindering the development of intelligent monitoring, analysis, and related applications in such scenes. To address this gap, we introduce PhysScene, the first SG dataset tailored to physics experiments. PhysScene encompasses specialized instruments, structured experimental setups, and functional relations intrinsic to experimental environments, enabling reasoning that extends beyond spatial co-occurrence to logical dependencies. Rather than pursuing large data scale, PhysScene focuses on strong semantic constraints and high relation density in experimental scenes, posing new challenges for existing scene parsing algorithms while offering opportunities for further improvements. Extensive analyses and experiments show that PhysScene complements existing benchmarks and establishes a valuable testbed for advancing scientific visual reasoning. The dataset is publicly available at https://github.com/ZMH-SDUST/PhysScene.
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