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ShapeNet: An Information-Rich 3D Model Repository
Angel X. Chang,Thomas Funkhouser,Leonidas J. Guibas,Pat Hanrahan,Qixing Huang,Zimo Li,Silvio Savarese,Manolis Savva,Shuran Song,Hao Su,Jianxiong Xiao,Li Yi,Fisher Yu +12 more
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ShapeNet contains 3D models from a multitude of semantic categories and organizes them under the WordNet taxonomy, a collection of datasets providing many semantic annotations for each 3D model such as consistent rigid alignments, parts and bilateral symmetry planes, physical sizes, keywords, as well as other planned annotations.Abstract:
We present ShapeNet: a richly-annotated, large-scale repository of shapes represented by 3D CAD models of objects. ShapeNet contains 3D models from a multitude of semantic categories and organizes them under the WordNet taxonomy. It is a collection of datasets providing many semantic annotations for each 3D model such as consistent rigid alignments, parts and bilateral symmetry planes, physical sizes, keywords, as well as other planned annotations. Annotations are made available through a public web-based interface to enable data visualization of object attributes, promote data-driven geometric analysis, and provide a large-scale quantitative benchmark for research in computer graphics and vision. At the time of this technical report, ShapeNet has indexed more than 3,000,000 models, 220,000 models out of which are classified into 3,135 categories (WordNet synsets). In this report we describe the ShapeNet effort as a whole, provide details for all currently available datasets, and summarize future plans.read more
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3D-GMNet: Learning to Estimate 3D Shape from A Single Image As A Gaussian Mixture.
TL;DR: The proposed 3D-GMNet is trained end-to-end with single input images and corresponding 3D models by using two novel loss functions: a 3D Gaussian mixture loss and a multi-view 2D loss that maximizes the likelihood of theGaussian mixture shape representation.
Journal ArticleDOI
Residue Assignment in Crystallographic Protein Electron Density Maps With 3D Convolutional Networks
TL;DR: In this paper , a neural network architecture, called 3D FC-DenseNet, is proposed for assigning amino acid labels to X-ray crystallographic electron density maps without relying on the amino acid sequence of proteins.
Book ChapterDOI
SPE-Net: Boosting Point Cloud Analysis via Rotation Robustness Enhancement
TL;DR: Zhang et al. as mentioned in this paper proposed a novel deep architecture tailored for 3D point cloud applications, named as SPE-Net, which relies on an attention mechanism that can effectively attend to the underlying rotation condition of the input.
Journal ArticleDOI
VPFusion: Joint 3D Volume and Pixel-Aligned Feature Fusion for Single and Multi-view 3D Reconstruction
Jisan Mahmud,Jan-Michael Frahm +1 more
TL;DR: This work proposes a novel interleaved 3D reasoning and pairwise view association architecture for feature volume fusion across different views and shows improved 3D reconstruction performance compared to existing methods.
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Cylindrical Convolutional Networks for Joint Object Detection and Viewpoint Estimation
TL;DR: Cylindrical convolutional networks (CCNs) as mentioned in this paper exploit cylindrical representation of a convolution kernel defined in the 3D space to predict object category scores at each viewpoint and simultaneously determine objective category and viewpoints using the proposed sinusoidal softargmax module.
References
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Proceedings ArticleDOI
ImageNet: A large-scale hierarchical image database
TL;DR: A new database called “ImageNet” is introduced, a large-scale ontology of images built upon the backbone of the WordNet structure, much larger in scale and diversity and much more accurate than the current image datasets.
Journal ArticleDOI
The Protein Data Bank
Helen M. Berman,John D. Westbrook,Zukang Feng,Gary L. Gilliland,Talapady N. Bhat,Helge Weissig,Ilya N. Shindyalov,Philip E. Bourne +7 more
TL;DR: The goals of the PDB are described, the systems in place for data deposition and access, how to obtain further information and plans for the future development of the resource are described.
Journal ArticleDOI
WordNet: a lexical database for English
TL;DR: WordNet1 provides a more effective combination of traditional lexicographic information and modern computing, and is an online lexical database designed for use under program control.
ReportDOI
Building a large annotated corpus of English: the penn treebank
TL;DR: As a result of this grant, the researchers have now published on CDROM a corpus of over 4 million words of running text annotated with part-of- speech (POS) tags, which includes a fully hand-parsed version of the classic Brown corpus.
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.