Multi-level Encoding with Hierarchical Alignment for Sketch-Based 3D Shape Retrieval

Sketch-based 3D shape retrieval (SBSR) aims to retrieve 3D shapes using hand-drawn sketches as query inputs. Although existing SBSR methods have achieved promising results, several challenges still require further investigation. First, most existing approaches usually leverage simple aggregation schemes, often failing to capture the intrinsic relationships between views, which limits the effectiveness of 3D shape feature extraction. Second, conventional SBSR primarily focuses on instance-level alignment while ignoring multi-level alignment, which may neglect complex hierarchical relationships. To address these limitations, we propose a novel Multi-level Encoding with Hierarchical Alignment (MEHA) method for SBSR. Specifically, we adopt spatial encoding and view encoding for multiple views of 3D shapes. The proposed aggregation scheme then integrates these multi-level embedded local features to enhance the representation of 3D shape features. Considering the complexity of 3D shapes, MEHA adopts a two-stage training process: the first stage focuses on learning 3D shape features, while the second stage emphasizes modality alignment. Furthermore, we introduce a hierarchical alignment strategy that bridges the modality gap through instance-level, prototype-level, and centre-level alignment. Extensive experiments on two public benchmark datasets demonstrate the superiority of our method, showing that MEHA outperforms the state-of-the-art baselines.