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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:Â
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
Learning Discriminative and Generative Shape Embeddings for Three-Dimensional Shape Retrieval
TL;DR: A novel encoder-decoder recurrent feature aggregation network (ERFA-Net) is presented, aimed at emphasizing the 3D properties of 3D shapes in the fusion of multiple view features, which achieves the state-of-the-art performance for 3D shape retrieval.
Journal ArticleDOI
Deep shape-aware descriptor for nonrigid 3D object retrieval
Hamed Ghodrati,A. Ben Hamza +1 more
TL;DR: This paper proposes a deep learning approach for 3D shape retrieval using a multi-level feature learning methodology that constructs mid-level features from these local descriptors via the bag-of-features paradigm and builds a deep shape-aware descriptor that is compact, geometrically informative and efficient to compute.
Proceedings ArticleDOI
Non-rigid 3D Model Classification Using 3D Hahn Moment Convolutional Neural Networks
Abderrahim Mesbah,Aissam Berrahou,Hicham Hammouchi,Hassan Berbia,Hassan Qjidaa,Mohamed Daoudi +5 more
TL;DR: Results show the significantly high performance in the classification rates of the proposed model and its ability to decrease the computational cost by training low number of features generated by the first 3D moments layer.
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Deep Structure for end-to-end inverse rendering.
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Feature Extraction Model for Social Images
TL;DR: This model extracts color, texture, geometric, and regional features from given image and also checks presence or absence of people in an image by face detection and is able to deal with the heterogeneous nature of social images.
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