Title | ||
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View Inter-Prediction GAN: Unsupervised Representation Learning for 3D Shapes by Learning Global Shape Memories to Support Local View Predictions |
Abstract | ||
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In this paper, we present a novel unsupervised representation learning approach for 3D shapes, which is an important research challenge as it avoids the manual effort required for collecting supervised data. Our method trains an RNN-based neural network architecture to solve multiple view inter-prediction tasks for each shape. Given several nearby views of a shape, we define view inter-prediction as the task of predicting the center view between the input views, and reconstructing the input views in a low-level feature space. The key idea of our approach is to implement the shape representation as a shape-specific global memory that is shared between all local view inter-predictions for each shape. Intuitively, this memory enables the system to aggregate information that is useful to better solve the view inter-prediction tasks for each shape, and to leverage the memory as a view-independent shape representation. Our approach obtains the best results using a combination of L-2 and adversarial losses for the view inter-prediction task. We show that VIP-GAN outperforms state-of-the-art methods in unsupervised 3D feature learning on three large-scale 3D shape benchmarks. |
Year | Venue | Field |
---|---|---|
2018 | national conference on artificial intelligence | Feature vector,3d shapes,Computer science,Neural network architecture,Artificial intelligence,Machine learning,Feature learning |
DocType | Volume | Citations |
Journal | abs/1811.02744 | 9 |
PageRank | References | Authors |
0.45 | 8 | 4 |
Name | Order | Citations | PageRank |
---|---|---|---|
Han Zhizhong | 1 | 198 | 18.28 |
Mingyang Shang | 2 | 42 | 3.09 |
Yu-shen Liu | 3 | 319 | 23.20 |
Zwicker Matthias | 4 | 2513 | 129.25 |