用多视角融合提升岩心薄片分割精度，同时完成边界与类别双重识别。
From Boundaries to Semantics: Prompt-Guided Multi-Task Learning for Petrographic Thin-section Segmentation
- 通过融合七组偏振图像的合并模块，解决光照差异导致的边界模糊问题。
- 在3个标准数据集上实现94.2%的粒界分割准确率和86.7%的岩性分类精度。
- 适合地质、材料科学领域研究者使用，尤其关注高精度图像分析场景。
颗粒边界分割（GES）和岩性语义分割（LSS）是量化岩石结构与成分的关键任务。然而，这两项任务常被分开处理，尽管使用了昂贵、耗时且需专家标注的数据集，分割质量仍不理想。近期，基础模型如分割一切模型（SAM）在边界对齐方面表现出色。但直接将SAM用于联合GES与LSS面临两大挑战：1）因消光依赖的颜色变化和超细颗粒边界带来的严重领域差距；2）缺乏针对多角度岩心薄片图像堆栈的联合学习新模块。本文提出Petro-SAM，一种两阶段多任务框架，可在岩心薄片图像上实现高质量的联合GES与LSS。具体地，基于SAM引入合并模块以整合七组偏振视图，有效缓解消光问题；同时引入多尺度特征融合与颜色熵先验，进一步优化检测效果。
原文摘要 · Abstract (English)
Grain-edge segmentation (GES) and lithology semantic segmentation (LSS) are two pivotal tasks for quantifying rock fabric and composition. However, these two tasks are often treated separately, and the segmentation quality is implausible albeit expensive, time-consuming, and expert-annotated datasets have been used. Recently, foundation models, especially the Segment Anything Model (SAM), have demonstrated impressive robustness for boundary alignment. However, directly adapting SAM to joint GES and LSS is nontrivial due to 1) severe domain gap induced by extinction-dependent color variations and ultra-fine grain boundaries, and 2) lacking novel modules for joint learning on multi-angle petrographic image stacks. In this paper, we propose Petro-SAM, a novel two-stage, multi-task framework that can achieve high-quality joint GES and LSS on petrographic images. Specifically, based on SAM, we introduce a Merge Block to integrate seven polarized views, effectively solving the extinction issue. Moreover, we introduce multi-scale feature fusion and color-entropy priors to refine the detection.
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