Open AccessPosted Content
Deep Learning Representation using Autoencoder for 3D Shape Retrieval
TLDR
This work projects 3D shapes into 2D space and uses autoencoder for feature learning on the 2D images and shows the proposed deep learning feature is complementary to conventional local image descriptors, which can obtain the state-of-the-art performance on 3D shape retrieval benchmarks.Abstract:
We study the problem of how to build a deep learning representation for 3D shape. Deep learning has shown to be very effective in variety of visual applications, such as image classification and object detection. However, it has not been successfully applied to 3D shape recognition. This is because 3D shape has complex structure in 3D space and there are limited number of 3D shapes for feature learning. To address these problems, we project 3D shapes into 2D space and use autoencoder for feature learning on the 2D images. High accuracy 3D shape retrieval performance is obtained by aggregating the features learned on 2D images. In addition, we show the proposed deep learning feature is complementary to conventional local image descriptors. By combing the global deep learning representation and the local descriptor representation, our method can obtain the state-of-the-art performance on 3D shape retrieval benchmarks.read more
Citations
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Journal ArticleDOI
DeepPano: Deep Panoramic Representation for 3-D Shape Recognition
TL;DR: This letter introduces a robust representation of 3-D shapes, named DeepPano, learned with deep convolutional neural networks (CNN), where a row-wise max-pooling layer is inserted between the convolution and fully-connected layers, making the learned representations invariant to the rotation around a principle axis.
Proceedings Article
Learning Representations and Generative Models for 3D Point Clouds.
TL;DR: In this article, a deep AutoEncoder (AE) network with state-of-the-art reconstruction quality and generalization ability is proposed. But the model is not suitable for 3D point clouds.
Journal ArticleDOI
Deep Learning Advances in Computer Vision with 3D Data: A Survey
TL;DR: It is concluded that systems employing 2D views of 3D data typically surpass voxel-based (3D) deep models, which however, can perform better with more layers and severe data augmentation, therefore, larger-scale datasets and increased resolutions are required.
Proceedings ArticleDOI
3D deep shape descriptor
TL;DR: Novel techniques to extract concise but geometrically informative shape descriptor and new methods of defining Eigen-shape descriptor and Fisher-shape descriptors to guide the training of a deep neural network are developed.
Journal ArticleDOI
Automatic Fabric Defect Detection with a Multi-Scale Convolutional Denoising Autoencoder Network Model
Shuang Mei,Yudan Wang,Guojun Wen +2 more
TL;DR: This paper proposes an unsupervised learning-based automated approach to detect and localize fabric defects without any manual intervention, used to reconstruct image patches with a convolutional denoising autoencoder network at multiple Gaussian pyramid levels and to synthesize detection results from the corresponding resolution channels.
References
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Journal ArticleDOI
On Visual Similarity Based 3D Model Retrieval
TL;DR: A visual similarity‐based 3D model retrieval system that is robust against similarity transformation, noise, model degeneracy, and provides 42%, 94% and 25% better performance than three other competing approaches.
Proceedings ArticleDOI
Rotation invariant spherical harmonic representation of 3D shape descriptors
TL;DR: The limitations of canonical alignment are described and an alternate method, based on spherical harmonics, for obtaining rotation invariant representations is discussed, which reduces the dimensionality of the descriptor, providing a more compact representation, which in turn makes comparing two models more efficient.
Book ChapterDOI
Coarse-to-Fine Auto-Encoder Networks (CFAN) for Real-Time Face Alignment
TL;DR: This paper proposes a Coarse-to-Fine Auto-encoder Networks (CFAN) approach, which cascades a few successive Stacked Auto- Encoding Networks (SANs) so that the first SAN predicts the landmarks quickly but accurately enough as a preliminary, by taking as input a low-resolution version of the detected face holistically.
Journal ArticleDOI
Shape-based image retrieval using generic Fourier descriptor
Dengsheng Zhang,Guojun Lu +1 more
TL;DR: A generic Fourier descriptor (GFD) is proposed to overcome the drawbacks of existing shape representation techniques by applying two-dimensional Fourier transform on a polar-raster sampled shape image.
Proceedings Article
Using very deep autoencoders for content-based image retrieval.
TL;DR: This work shows how to learn many layers of features on color images and how these features are used to initialize deep autoencoders, which are then used to map images to short binary codes.