In recent years, sketch-based 3D shape retrieval has attracted growing attention. While many previous studies have focused on cross-modal matching between hand-drawn sketches and 3D shapes, the critical issue of how to handle low-quality and noisy samples in sketch data has been largely neglected. This paper presents an uncertainty-aware cross-modal transfer network (UACTN) that addresses this issue. UACTN decouples the representation learning of sketches and 3D shapes into two separate tasks: classification-based sketch uncertainty learning and 3D shape feature transfer. We first introduce an end-to-end classification-based approach that simultaneously learns sketch features and uncertainty, allowing uncertainty to prevent overfitting noisy sketches by assigning different levels of importance to clean and noisy sketches. Then, 3D shape features are mapped into the pre-learned sketch embedding space for feature alignment. Extensive experiments and ablation studies on two benchmarks demonstrate the superiority of our proposed method compared to state-of-the-art methods.

翻译：近年来，基于草图的三维形状检索受到越来越多的关注。尽管许多先前研究聚焦于手绘草图与三维形状之间的跨模态匹配，但如何处理草图数据中的低质量与噪声样本这一关键问题在很大程度上被忽视了。本文提出了一种不确定性感知的跨模态迁移网络（UACTN）来解决该问题。UACTN将草图与三维形状的表征学习解耦为两个独立任务：基于分类的草图不确定性学习与三维形状特征迁移。我们首先引入一种端到端的基于分类方法，该方法同时学习草图特征与不确定性，通过为干净草图与噪声草图分配不同重要程度，使不确定性能够防止对噪声草图的过拟合。随后，三维形状特征被映射至预学习的草图嵌入空间以实现特征对齐。在两个基准数据集上的大量实验与消融研究表明，我们提出的方法与最先进方法相比具有优越性。