Contrastive Multi-View Learning for 3D Shape Clustering

Unsupervised 3D shape clustering is emerging as a promising research topic in multimedia and computer vision field. Considering the flexibility of acquiring multiple views for 3D shapes, this paper proposes a contrastive multi-view learning network (CMVL-Net) to cluster unlabeled 3D shapes from multiple views. To the best of our knowledge, this is the first multi-view-oriented 3D shape deep clustering method. The key to this method lies in how to capture highly discriminative 3D shape features suitable for clustering. By exploring consistency and complementarity among multiple views, a cross-view contrastive clustering mechanism is proposed to learn clustering-specified discriminative 3D shape features. To obtain a more compact 3D shape clustering structure, a consensus graph-guided contrastive constraint is designed to encourage cluster-wise consistency learning under the guidance of potential category associations among shapes. Experimental results on two widely used benchmark datasets demonstrate the effectiveness of the proposed method.