A Progressive Multi-View Learning Approach for Multi-Loss Optimization in 3D Object Recognition

3D object recognition is a well studied 2D multi-view object classification task that achieves high accuracy if the object textures are distinctive. However, if objects are texture-less and are only differentiable by their shapes but at certain viewpoints. Thus, the problem is still very challenging. Furthermore, the existing methods are mostly based on supervised learning with lots of images per object which are difficult to collect and label them for training. In this letter, we introduced a multi-loss view invariant stochastic prototype embedding to minimize and improve the recognition accuracy of novel objects at different viewpoints by using a progressive multi-view learning approach. An extensive experimental results show that the proposed method outperforms the state-of-the-art methods on different types datasets and also on different backbones.