Journal of East China Normal University(Natural Science) ›› 2023, Vol. 2023 ›› Issue (6): 61-72.doi: 10.3969/j.issn.1000-5641.2023.06.006

• Computer Science • Previous Articles     Next Articles

Momentum-updated representation with reconstruction constraint for limited-view 3D object recognition

Ruibo CUI, Feng WANG*()   

  1. School of Computer Science and Technology, East China Normal University, Shanghai 200062, China
  • Received:2022-06-18 Online:2023-11-25 Published:2023-11-23
  • Contact: Feng WANG


We propose a neural network training framework called momentum-updated representation with reconstruction constraint for 3D (three-dimensional) object recognition using 2D (two-dimensional) images without angle labels. First, self-supervised learning is employed to address the lack of angle labels. Second, we use momentum updating based on a dynamic queue to maintain the stability of the object representation. Furthermore, the reconstruction constraint is applied to the learning process with an auto-encoder module, which enables the representation to capture more semantic information of the objects. Finally, during training, a dynamic queue reduction strategy is proposed for handling the imbalanced data distribution. Experiments on two popular multi-view datasets, ModelNet and ShapeNet, demonstrate that the proposed method outperforms existing methods.

Key words: multi-view object recognition, self-supervised learning, auto-encoder

CLC Number: