Hierarchical View Predictor: Unsupervised 3D Global Feature Learning through Hierarchical Prediction among Unordered Views

by   Zhizhong Han, et al.

Unsupervised learning of global features for 3D shape analysis is an important research challenge because it avoids manual effort for supervised information collection. In this paper, we propose a view-based deep learning model called Hierarchical View Predictor (HVP) to learn 3D shape features from unordered views in an unsupervised manner. To mine highly discriminative information from unordered views, HVP performs a novel hierarchical view prediction over a view pair, and aggregates the knowledge learned from the predictions in all view pairs into a global feature. In a view pair, we pose hierarchical view prediction as the task of hierarchically predicting a set of image patches in a current view from its complementary set of patches, and in addition, completing the current view and its opposite from any one of the two sets of patches. Hierarchical prediction, in patches to patches, patches to view and view to view, facilitates HVP to effectively learn the structure of 3D shapes from the correlation between patches in the same view and the correlation between a pair of complementary views. In addition, the employed implicit aggregation over all view pairs enables HVP to learn global features from unordered views. Our results show that HVP can outperform state-of-the-art methods under large-scale 3D shape benchmarks in shape classification and retrieval.


page 1

page 2

page 4

page 5

page 6

page 7

page 8

page 9


3DViewGraph: Learning Global Features for 3D Shapes from A Graph of Unordered Views with Attention

Learning global features by aggregating information over multiple views ...

Self-supervised Discriminative Feature Learning for Multi-view Clustering

Multi-view clustering is an important research topic due to its capabili...

3D-Assisted Image Feature Synthesis for Novel Views of an Object

Comparing two images in a view-invariant way has been a challenging prob...

Semi-Supervised Co-Analysis of 3D Shape Styles from Projected Lines

We present a semi-supervised co-analysis method for learning 3D shape st...

Feature Learning in Image Hierarchies using Functional Maximal Correlation

This paper proposes the Hierarchical Functional Maximal Correlation Algo...

Parts4Feature: Learning 3D Global Features from Generally Semantic Parts in Multiple Views

Deep learning has achieved remarkable results in 3D shape analysis by le...

Please sign up or login with your details

Forgot password? Click here to reset