Monocular Viewpoints Estimation for Generic Objects in the Wild

Abstract

Although classification-based approaches achieved remarkable performance in viewpoints estimation, they still have limitations and face the challenging viewpoints ambiguity problem. In this paper, we analyze this problem and give solutions: 1) we propose Viewpoint Discernibility Matrix (VDM) loss, which is a more suitable loss than the one-hot cross-entropy loss by tolerating the sub-optimal predictions and penalizing the wrong predictions on ambiguous viewpoints; and 2) we propose Auxiliary Hierarchical Viewpoints Supervision (AHVS) method, which is able to restrain the network to pay closer attention to the features of ambiguous viewpoints. Training with VDM loss and AHVS, the model is endowed with strong representation ability to achieve significant improvements on viewpoints estimation. Extensive experiments are conducted and show the superiority of our approach. On the large Pascal3D+ dataset, we achieve the state-of-The-Art results among all commonly used metrics.