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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
TLDR
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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PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space
TL;DR: A hierarchical neural network that applies PointNet recursively on a nested partitioning of the input point set and proposes novel set learning layers to adaptively combine features from multiple scales to learn deep point set features efficiently and robustly.
Journal ArticleDOI
Dynamic Graph CNN for Learning on Point Clouds
TL;DR: This work proposes a new neural network module suitable for CNN-based high-level tasks on point clouds, including classification and segmentation called EdgeConv, which acts on graphs dynamically computed in each layer of the network.
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NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis
Ben Mildenhall,Pratul P. Srinivasan,Matthew Tancik,Jonathan T. Barron,Ravi Ramamoorthi,Ren Ng +5 more
TL;DR: This work describes how to effectively optimize neural radiance fields to render photorealistic novel views of scenes with complicated geometry and appearance, and demonstrates results that outperform prior work on neural rendering and view synthesis.
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Fast Graph Representation Learning with PyTorch Geometric
Matthias Fey,Jan Eric Lenssen +1 more
TL;DR: PyTorch Geometric is introduced, a library for deep learning on irregularly structured input data such as graphs, point clouds and manifolds, built upon PyTorch, and a comprehensive comparative study of the implemented methods in homogeneous evaluation scenarios is performed.
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.
References
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Proceedings ArticleDOI
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Proceedings ArticleDOI
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Proceedings ArticleDOI
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Proceedings ArticleDOI
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Journal ArticleDOI
A benchmark for 3D mesh segmentation
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