Learning Discriminative 3D Shape Representations by View Discerning Networks

VSFormer: Mining Correlations in Flexible View Set for Multi-View 3D Shape Understanding

View-based methods have demonstrated promising performance in 3D shape understanding. However, they tend to make strong assumptions about the relations between views or learn the multi-view correlations indirectly, which limits the flexibility of exploring inter-view correlations and the effectiveness of target tasks. To overcome the above problems, this article investigates flexible organization and explicit correlation learning for multiple views. In particular, we propose to incorporate different views of a 3D shape into a permutation-invariant set, referred to as View Set, which removes rigid relation assumptions and facilitates adequate information exchange and fusion among views. Based on that, we devise a nimble Transformer model, named VSFormer, to explicitly capture pairwise and higher-order correlations of all elements in the set. Meanwhile, we theoretically reveal a natural correspondence between the Cartesian product of a view set and the correlation matrix in the attention mechanism, which supports our model design. Comprehensive experiments suggest that VSFormer has better flexibility, efficient inference efficiency and superior performance. Notably, VSFormer reaches state-of-the-art results on various 3 d recognition datasets, including ModelNet40, ScanObjectNN and RGBD. It also establishes new records on the SHREC'17 retrieval benchmark.

Soft Margin Triplet-Center Loss for Multi-View 3D Shape Retrieval

Obtaining discriminative features is one of the key problems in three-dimensional (3D) shape retrieval. Recently, deep metric learning-based 3D shape retrieval methods have attracted the researchers’ attention and have achieved better performance. The triplet-center loss can learn more discriminative features than traditional classification loss, and it has been successfully used in deep metric learning-based 3D shape retrieval task. However, it has a hard margin parameter that only leverages part of the training data in each mini-batch. Moreover, the margin parameter is often determined by experience and remains unchanged during the training process. To overcome the above limitations, we propose the soft margin triplet-center loss, which replaces the margin with the nonparametric soft margin. Furthermore, we combined the proposed soft margin triplet-center loss with the softmax loss to improve the training efficiency and the retrieval performance. Extensive experimental results on two popular 3D shape retrieval datasets have validated the effectiveness of the soft margin triplet-center loss, and our proposed 3D shape retrieval method has achieved better performance than other state-of-the-art method.

Multi-Scale Representation Learning on Hypergraph for 3D Shape Retrieval and Recognition

Effective 3D shape retrieval and recognition are challenging but important tasks in computer vision research field, which have attracted much attention in recent decades. Although recent progress has shown significant improvement of deep learning methods on 3D shape retrieval and recognition performance, it is still under investigated of how to jointly learn an optimal representation of 3D shapes considering their relationships. To tackle this issue, we propose a multi-scale representation learning method on hypergraph for 3D shape retrieval and recognition, called multi-scale hypergraph neural network (MHGNN). In this method, the correlation among 3D shapes is formulated in a hypergraph and a hypergraph convolution process is conducted to learn the representations. Here, multiple representations can be obtained through different convolution layers, leading to multi-scale representations of 3D shapes. A fusion module is then introduced to combine these representations for 3D shape retrieval and recognition. The main advantages of our method lie in 1) the high-order correlation among 3D shapes can be investigated in the framework and 2) the joint multi-scale representation can be more robust for comparison. Comparisons with state-of-the-art methods on the public ModelNet40 dataset demonstrate remarkable performance improvement of our proposed method on the 3D shape retrieval task. Meanwhile, experiments on recognition tasks also show better results of our proposed method, which indicate the superiority of our method on learning better representation for retrieval and recognition.