Journal ArticleDOI
DeepShape: Deep-Learned Shape Descriptor for 3D Shape Retrieval
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TLDR
This paper proposes a novel 3D shape feature learning method to extract high-level shape features that are insensitive to geometric deformations of shapes using a discriminative deep auto-encoder to learn deformation-invariant shape features.Abstract:
Complex geometric variations of 3D models usually pose great challenges in 3D shape matching and retrieval. In this paper, we propose a novel 3D shape feature learning method to extract high-level shape features that are insensitive to geometric deformations of shapes. Our method uses a discriminative deep auto-encoder to learn deformation-invariant shape features. First, a multiscale shape distribution is computed and used as input to the auto-encoder. We then impose the Fisher discrimination criterion on the neurons in the hidden layer to develop a deep discriminative auto-encoder. Finally, the outputs from the hidden layers of the discriminative auto-encoders at different scales are concatenated to form the shape descriptor. The proposed method is evaluated on four benchmark datasets that contain 3D models with large geometric variations: McGill, SHREC’10 ShapeGoogle, SHREC’14 Human and SHREC’14 Large Scale Comprehensive Retrieval Track Benchmark datasets. Experimental results on the benchmark datasets demonstrate the effectiveness of the proposed method for 3D shape retrieval.read more
Citations
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Proceedings ArticleDOI
Relation-Shape Convolutional Neural Network for Point Cloud Analysis
TL;DR: RS-CNN as mentioned in this paper extends regular grid CNN to irregular configuration for point cloud analysis, where the convolutional weight for local point set is forced to learn a highlevel relation expression from predefined geometric priors, between a sampled point from this point set and the others.
Proceedings ArticleDOI
GVCNN: Group-View Convolutional Neural Networks for 3D Shape Recognition
TL;DR: Experimental results and comparison with state-of-the-art methods show that the proposed GVCNN method can achieve a significant performance gain on both the 3D shape classification and retrieval tasks.
Journal ArticleDOI
Learning Rotation-Invariant and Fisher Discriminative Convolutional Neural Networks for Object Detection
TL;DR: This work builds up the existing state-of-the-art object detection systems and proposes a simple but effective method to train rotation-invariant and Fisher discriminative CNN models to further boost object detection performance.
Posted Content
Relation-Shape Convolutional Neural Network for Point Cloud Analysis
TL;DR: RS-CNN as mentioned in this paper extends regular grid CNN to irregular configuration for point cloud analysis, where the convolutional weight for local point set is forced to learn a highlevel relation expression from predefined geometric priors, between a sampled point from this point set and the others.
Proceedings ArticleDOI
Triplet-Center Loss for Multi-view 3D Object Retrieval
TL;DR: Wang et al. as discussed by the authors proposed triplet-center loss, which learns a center for each class and requires that the distances between samples and centers from the same class are closer than those from different classes.
References
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Reducing the Dimensionality of Data with Neural Networks
TL;DR: In this article, an effective way of initializing the weights that allows deep autoencoder networks to learn low-dimensional codes that work much better than principal components analysis as a tool to reduce the dimensionality of data is described.
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TL;DR: The motivations and principles regarding learning algorithms for deep architectures, in particular those exploiting as building blocks unsupervised learning of single-layer modelssuch as Restricted Boltzmann Machines, used to construct deeper models such as Deep Belief Networks are discussed.
Proceedings ArticleDOI
3D ShapeNets: A deep representation for volumetric shapes
TL;DR: This work proposes to represent a geometric 3D shape as a probability distribution of binary variables on a 3D voxel grid, using a Convolutional Deep Belief Network, and shows that this 3D deep representation enables significant performance improvement over the-state-of-the-arts in a variety of tasks.
Journal ArticleDOI
Shape distributions
TL;DR: The dissimilarities between sampled distributions of simple shape functions provide a robust method for discriminating between classes of objects in a moderately sized database, despite the presence of arbitrary translations, rotations, scales, mirrors, tessellations, simplifications, and model degeneracies.