Deepshape: Deep learned shape descriptor for 3D shape matching and retrieval

By J. Xie, Yi Fang, Fan Zhu, Edward K. Wong. 2015

138 citations recorded by the source corpus.

Subfields: Computational Mechanics, Computer Graphics and Computer-Aided Design, Computer Vision and Pattern Recognition

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In Computational Mechanics

In Computer Graphics and Computer-Aided Design

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Citations in the corpus

Deep Sliding Shapes for Amodal 3D Object Detection in RGB-D Images (2015, methodology): “Furthermore, compared to methods using hand-crafted 3D features [5, 31], we desire a representation that encodes the 3D geometry as raw as possible, and let ConvNets learn the most discriminative features from the raw data.”
Learning Weight Uncertainty with Stochastic Gradient MCMC for Shape Classification (2016, background): “The first is the use of deep models with stochastic hidden layers, including deep belief networks [9] or the autoencoder [80]; these methods discover latent representations of shapes from their hand-crafted features, such as spectral descriptors [2, 67].”
Learning Semantic Deformation Flows with 3D Convolutional Networks (2016, background): “3D feature extraction using fully connected autoencoders [10, 41] and multi-view based CNNs [38] are also actively studied for classification and retrieval.”
GVCNN: Group-View Convolutional Neural Networks for 3D Shape Recognition (2018, background): “Xie et al. [28, 27] proposed a deep auto-encoder for 3D shape feature extraction.”
Deep Learning Advances in Computer Vision with 3D Data (2017, methodology): “The distributions of HKSs of shape at different scales were extracted as low-level descriptors from 3D objects in Xie et al. [2015a].”
Learning a Hierarchical Latent-Variable Model of 3D Shapes (2017, background): “Following this key study, researchers have tried applying 3D ConvNets [27, 5, 42, 48], autoencoders [47, 49, 13, 32], and a variety of probabilistic neural generative models [44, 32] to the problem of 3D model recognition, with each study progressively advancing state-of-the-art.”
GIFT: A Real-Time and Scalable 3D Shape Search Engine (2016, background): “However, efﬁciency issue has been more or less ignored by previous works, though enormous efforts have been devoted to retrieval effectiveness , that is to say, to de-sign informative and discriminative features [12, 2, 17, 6, 41, 15, 18] to boost the retrieval accuracy.”
RIFD-CNN: Rotation-Invariant and Fisher Discriminative Convolutional Neural Networks for Object Detection (2016, background): “Consequently, our work is also partly related with the ideas of learning invariant features such as [40-46] and the approaches incorporating discriminative terms into model training such as [47, 48].”