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Guoxian Dai

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2 papers

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IJCAI Conference 2018 Conference Paper

Siamese CNN-BiLSTM Architecture for 3D Shape Representation Learning

Guoxian Dai
Jin Xie
Yi Fang

Learning a 3D shape representation from a collection of its rendered 2D images has been extensively studied. However, existing view-based techniques have not yet fully exploited the information among all the views of projections. In this paper, by employing recurrent neural network to efficiently capture features across different views, we propose a siamese CNN-BiLSTM network for 3D shape representation learning. The proposed method minimizes a discriminative loss function to learn a deep nonlinear transformation, mapping 3D shapes from the original space into a nonlinear feature space. In the transformed space, the distance of 3D shapes with the same label is minimized, otherwise the distance is maximized to a large margin. Specifically, the 3D shapes are first projected into a group of 2D images from different views. Then convolutional neural network (CNN) is adopted to extract features from different view images, followed by a bidirectional long short-term memory (LSTM) to aggregate information across different views. Finally, we construct the whole CNN-BiLSTM network into a siamese structure with contrastive loss function. Our proposed method is evaluated on two benchmarks, ModelNet40 and SHREC 2014, demonstrating superiority over the state-of-the-art methods.

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AAAI Conference 2017 Conference Paper

Deep Correlated Metric Learning for Sketch-based 3D Shape Retrieval

Guoxian Dai
Jin Xie
Fan Zhu
Yi Fang

The explosive growth of 3D models has led to the pressing demand for an efﬁcient searching system. Traditional modelbased search is usually not convenient, since people don’t always have 3D model available by side. The sketch-based 3D shape retrieval is a promising candidate due to its simpleness and efﬁciency. The main challenge for sketch-based 3D shape retrieval is the discrepancy across different domains. In the paper, we propose a novel deep correlated metric learning (DCML) method to mitigate the discrepancy between sketch and 3D shape domains. The proposed DCML trains two distinct deep neural networks (one for each domain) jointly with one loss, which learns two deep nonlinear transformations to map features from both domains into a nonlinear feature space. The proposed loss, including discriminative loss and correlation loss, aims to increase the discrimination of features within each domain as well as the correlation between different domains. In the transfered space, the discriminative loss minimizes the intra-class distance of the deep transformed features and maximizes the inter-class distance of the deep transformed features at least a predeﬁned margin within each domain, while the correlation loss focuses on minimizing the distribution discrepancy across different domains. Our proposed method is evaluated on SHREC 2013 and 2014 benchmarks, and the experimental results demonstrate the superiority of our proposed method over the state-of-the-art methods.

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