Image: Sun et al
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However, there is little effort on using mesh data in recent years, due to the complexity and irregularity of mesh data.
It has been challenging to analyze signals with mixed topologies (for example, point cloud with surface mesh).
Several popular approaches to 3D vision tasks process multiple views of the input independently with deep neural networks pre-trained on natural images, achieving view permutation invariance through a single round of pooling over all views.
Current best local descriptors are learned on a large dataset of matching and non-matching keypoint pairs.
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.
View-based approach that recognizes 3D shape through its projected 2D images has achieved state-of-the-art results for 3D shape recognition.
This paper proposes a novel probabilistic framework for the learning of unsupervised deep shape descriptors with point distribution learning.