Uncertainty-Aware Cross-Modal Transfer Network for Sketch-Based 3D Shape
  Retrieval
Yiyang Cai Jiaming Lu Jiewen Wang Shuang Liang
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.