Abstract
Most existing 3D object recognition algorithms focus on
leveraging the strong discriminative power of deep learning models with softmax loss for the classification of 3D
data, while learning discriminative features with deep metric learning for 3D object retrieval is more or less neglected.
In the paper, we study variants of deep metric learning
losses for 3D object retrieval, which did not receive enough
attention from this area. First , two kinds of representative
losses, triplet loss and center loss, are introduced which
could learn more discriminative features than traditional
classification loss. Then, we propose a novel loss named
triplet-center loss, which can further enhance the discriminative power of the features. The proposed triplet-center
loss learns a center for each class and requires that the distances between samples and centers from the same class are
closer than those from different classes. Extensive experimental results on two popular 3D object retrieval benchmarks and two widely-adopted sketch-based 3D shape retrieval benchmarks consistently demonstrate the effectiveness of our proposed loss, and significant improvements
have been achieved compared with the state-of-the-arts