AI 中文总结
针对叶片化石极少完整保存的问题，提出AmodalDINO模型，通过两项修改实现无需上游实例分割器的非模态重建，在合成与真实化石上表现优异，可离线运行并支持表面积估算与活体叶片可视化。
AI 中文摘要
叶片化石极少完整保存——沉积岩会隐藏、破损并侵蚀叶片的叶片组织，而古植物学却依赖叶片的完整形状和轮廓。我们将缺失组织的恢复视为非模态重建任务，并提出了AmodalDINO，这是一种多头密集预测模型，可从单张RGB图像中预测四种掩码：可见叶片、非模态完整叶片、非模态主脉以及细脉。与几乎所有现有的非模态工作不同，AmodalDINO未提供可见掩码，而是联合预测可见区域与非模态区域，因此运行时无需上游实例分割器。我们对模型做了两项简单但有效的修改以适配非模态分割任务：以较小学习率对DINOv3 ViT-L/16进行全微调，而非冻结该模型；在叶片头之外附加辅助脉序头。这两项修改使模型能够学习叶片的结构形状先验。仅在合成叶片化石图像上训练的AmodalDINO，在验证集上达到95.0%的Dice系数、90.5%的IoU，且能良好迁移至真实化石标本。精简为两个头后，相同方案可在两个基准数据集上运行，在KINS数据集上达到85.05的全mIoU、66.65的遮挡mIoU，在COCOA-cls数据集上达到80.90、38.15。该模型还具备实用性：通过量化为4位权重，可在浏览器中完全离线运行，与原模型的IoU匹配度达0.910。我们还添加了基于标尺的校准以估计表面积，并在本地设备上实现了活体叶片的生成式可视化。
英文摘要
Fossil leaves are rarely preserved whole -- sedimentary rock hides, breaks, and erodes the lamina, yet paleobotany depends on the complete shape and outline of the leaf. We cast the recovery of the missing tissue as amodal reconstruction and present AmodalDINO, a multi-head dense-prediction model that predicts four masks from a single RGB image: visible leaf, amodal complete leaf, amodal main vein, and fine veins. Unlike essentially all prior amodal work, AmodalDINO is given no visible mask. It predicts the visible and amodal regions jointly, so it needs no upstream instance segmenter at runtime. Two simple but effective changes adapt the model to the amodal segmentation task: fully fine-tune a DINOv3 ViT-L/16 at a small learning rate instead of freezing it, and attach auxiliary venation heads alongside the leaf heads. These two changes enable the model to learn the structural shape prior of leaves. Trained only on synthetic leaf fossil images, AmodalDINO reaches 95.0% Dice / 90.5% IoU on the validation set and transfers well to real fossil specimens. Stripped to two heads, the same recipe can run on two benchmark datasets, reaching 85.05 full mIoU / 66.65 occluded mIoU on KINS and 80.90 / 38.15 on COCOA-cls. The model is also practical: by quantizing to 4-bit weights, it runs entirely offline in a browser, matching the original model with an IoU of 0.910. We also add ruler-based calibration to estimate surface area, and a generative visualization of living leaves on local devices.
Comments12 pages, 10 figures
