Open AccessPosted Content
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
Citations
More filters
Posted Content
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.
Posted Content
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.
Posted Content
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
More filters
Book ChapterDOI
Retrieving articulated 3-d models using medial surfaces and their graph spectra
TL;DR: The results demonstrate that medial surface based graph matching significantly outperforms these techniques for objects with articulating parts.
Proceedings ArticleDOI
Building a database of 3D scenes from user annotations
Bryan Russell,Antonio Torralba +1 more
TL;DR: A model is described that integrates cues extracted from the object labels to infer the implicit geometric information and it is shown how it can find better scene matches for an unlabeled image by expanding the database through viewpoint interpolation to unseen views.
Proceedings ArticleDOI
SHREC'12 track: generic 3D shape retrieval
Bo Li,Afzal Godil,Masaki Aono,X. Bai,Takahiko Furuya,L. Li,Roberto J. López-Sastre,Henry Johan,Ryutarou Ohbuchi,Carolina Redondo-Cabrera,Atsushi Tatsuma,T. Yanagimachi,Shixiong Zhang +12 more
TL;DR: The aim of this track is to measure and compare the performance of generic 3D shape retrieval methods implemented by different participants over the world and their retrieval accuracies were evaluated using 7 commonly used performance metrics.
Journal ArticleDOI
Fine-grained semi-supervised labeling of large shape collections
TL;DR: A multi-label semi-supervised approach that takes as input a large shape collection of a given category with associated sparse and noisy labels, and outputs cleaned and complete labels for each shape, which yields results that are superior to state-of-the-art semi- supervised learning techniques.
Journal ArticleDOI
Creating consistent scene graphs using a probabilistic grammar
Tianqiang Liu,Siddhartha Chaudhuri,Vladimir G. Kim,Qixing Huang,Niloy J. Mitra,Thomas Funkhouser +5 more
TL;DR: The proposed algorithms can be used to provide consistent hierarchies for large collections of scenes within the same semantic class, and outperform alternative approaches that consider only shape similarities and/or spatial relationships without hierarchy.