Journal ArticleDOI
DeepPano: Deep Panoramic Representation for 3-D Shape Recognition
TLDR
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.Abstract:
This letter introduces a robust representation of 3-D shapes, named DeepPano, learned with deep convolutional neural networks (CNN). Firstly, each 3-D shape is converted into a panoramic view, namely a cylinder projection around its principle axis. Then, a variant of CNN is specifically designed for learning the deep representations directly from such views. Different from typical CNN, 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. Our approach achieves state-of-the-art retrieval/classification results on two large-scale 3-D model datasets (ModelNet-10 and ModelNet-40), outperforming typical methods by a large margin.read more
Citations
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Proceedings ArticleDOI
Volumetric and Multi-view CNNs for Object Classification on 3D Data
TL;DR: In this paper, two distinct network architectures of volumetric CNNs and multi-view CNNs are introduced, where they introduce multiresolution filtering in 3D. And they provide extensive experiments designed to evaluate underlying design choices.
Posted Content
Deep Sets
Manzil Zaheer,Satwik Kottur,Siamak Ravanbakhsh,Barnabás Póczos,Ruslan Salakhutdinov,Alexander J. Smola +5 more
TL;DR: The main theorem characterizes the permutation invariant objective functions and provides a family of functions to which any permutation covariant objective function must belong, which enables the design of a deep network architecture that can operate on sets and which can be deployed on a variety of scenarios including both unsupervised and supervised learning tasks.
Posted Content
Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling
TL;DR: Wang et al. as discussed by the authors proposed a 3D Generative Adversarial Network (3D-GAN), which generates 3D objects from a probabilistic space by leveraging recent advances in volumetric convolutional networks and generative adversarial nets.
Proceedings ArticleDOI
Deep Sliding Shapes for Amodal 3D Object Detection in RGB-D Images
Shuran Song,Jianxiong Xiao +1 more
TL;DR: This work proposes the first 3D Region Proposal Network (RPN) to learn objectness from geometric shapes and the first joint Object Recognition Network (ORN) to extract geometric features in 3D and color features in 2D.
Journal ArticleDOI
O-CNN: octree-based convolutional neural networks for 3D shape analysis
TL;DR: The O-CNN is presented, an Octree-based Convolutional Neural Network (CNN) for 3D shape analysis built upon the octree representation of 3D shapes, which takes the average normal vectors of a 3D model sampled in the finest leaf octants as input and performs 3D CNN operations on the octants occupied by the3D shape surface.
References
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Proceedings Article
ImageNet Classification with Deep Convolutional Neural Networks
TL;DR: The state-of-the-art performance of CNNs was achieved by Deep Convolutional Neural Networks (DCNNs) as discussed by the authors, which consists of five convolutional layers, some of which are followed by max-pooling layers, and three fully-connected layers with a final 1000-way softmax.
Journal ArticleDOI
Gradient-based learning applied to document recognition
Yann LeCun,Léon Bottou,Léon Bottou,Yoshua Bengio,Yoshua Bengio,Yoshua Bengio,Patrick Haffner +6 more
TL;DR: In this article, a graph transformer network (GTN) is proposed for handwritten character recognition, which can be used to synthesize a complex decision surface that can classify high-dimensional patterns, such as handwritten characters.
Journal Article
Dropout: a simple way to prevent neural networks from overfitting
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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.
Proceedings Article
Fast approximate nearest neighbors with automatic algorithm configuration
Marius Muja,David G. Lowe +1 more
TL;DR: A system that answers the question, “What is the fastest approximate nearest-neighbor algorithm for my data?” and a new algorithm that applies priority search on hierarchical k-means trees, which is found to provide the best known performance on many datasets.