基于局部外观的材料分层识别，提升真实场景下的泛化能力。
Hierarchical Material Recognition from Local Appearance
- 构建按物理特性排序的材料分类体系，支持分层识别。
- 在真实场景数据集上实现当前最优性能，且支持少样本学习。
- 利用深度图生成新视角，增强模型对复杂成像条件的适应性。
我们提出一种基于局部外观的材料分层识别分类体系，其分类依据源于视觉应用需求和材料的物理特性。为此，我们构建了一个多样化的野外采集数据集，包含分类类别对应的图像与深度图。基于该分类体系与数据集，我们提出一种基于图注意力网络的分层材料识别方法，充分利用类别间的层次邻近关系，实现了当前最佳性能。实验表明，该模型具备良好的泛化能力，可适应恶劣的真实成像条件；利用深度图生成的新视角图像能进一步提升其鲁棒性。此外，模型在少样本学习设置下展现出快速学习新材质的能力。
原文摘要 · Abstract (English)
We introduce a taxonomy of materials for hierarchical recognition from local appearance. Our taxonomy is motivated by vision applications and is arranged according to the physical traits of materials. We contribute a diverse, in-the-wild dataset with images and depth maps of the taxonomy classes. Utilizing the taxonomy and dataset, we present a method for hierarchical material recognition based on graph attention networks. Our model leverages the taxonomic proximity between classes and achieves state-of-the-art performance. We demonstrate the model's potential to generalize to adverse, real-world imaging conditions, and that novel views rendered using the depth maps can enhance this capability. Finally, we show the model's capacity to rapidly learn new materials in a few-shot learning setting.
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