用大模型分析3D高斯点云的演化路径，实现可解释的版权溯源。
GaussTrace: Provenance Analysis of 3D Gaussian Splatting Models with Evidence-based LLM Reasoning
- 基于参数统计与编辑模拟生成证据，驱动大模型推理
- 无需训练或历史记录，准确构建3DGS模型演化关系图
- 适合数字资产确权、侵权取证等场景
3D高斯点云（3DGS）是一种创建高保真3D资产的强大技术。然而，3DGS模型在数字平台间广泛共享与迭代修改，给知识产权保护和溯源带来严峻挑战。为此，我们提出GaussTrace，一种构建3DGS模型有向溯源图的新框架。GaussTrace将溯源分析建模为基于证据的推理问题，通过属性级统计分析3DGS参数以捕捉内在特征，并引入假设驱动的常见操作编辑模拟，提供潜在演变路径的辅助证据。统计与模拟线索共同支持大语言模型（LLM）进行结构化思维链（CoT）推理，生成方向性溯源推断及可解释的边理由。实验表明，GaussTrace能有效构建多种3DGS模型间的演化关系，无需模型训练或编辑历史访问即可生成准确、可解释且鲁棒的溯源图。
原文摘要 · Abstract (English)
3D Gaussian Splatting (3DGS) is a powerful technique for creating high-fidelity 3D assets. However, the widespread sharing and iterative modification of 3DGS models across digital platforms create pressing challenges for intellectual property protection and forensic traceability. To address this, we propose GaussTrace, a novel framework for constructing directed provenance graphs for 3DGS models. GaussTrace formulates provenance analysis as an evidence-based reasoning problem. It builds upon attribute-wise statistical profiling of 3DGS parameters to capture intrinsic properties. Moreover, we introduce hypothesis-driven editing simulations of common operations to provide auxiliary evidence for plausible transformation pathways. These statistical and simulated cues jointly enable a Large Language Model (LLM) to perform structured Chain-of-Thought (CoT) reasoning, yielding directional provenance inferences and explainable edge reasons. Experimental results demonstrate that GaussTrace effectively constructs evolutionary relationships among diverse 3DGS models, delivering accurate, interpretable, and robust provenance graphs without requiring model training or access to editing histories. Project page: https://haolianghan.github.io/GaussTrace.
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