Main ShapeNet tech report

Please cite the main tech report if you use ShapeNet in your research: ShapeNet: An Information-Rich 3D Model Repository (bib). This report provides annotation details and statistics.

  title       = {{ShapeNet: An Information-Rich 3D Model Repository}},
  author      = {Chang, Angel X. and Funkhouser, Thomas and Guibas, Leonidas and Hanrahan, Pat and Huang, Qixing and Li, Zimo and Savarese, Silvio and Savva, Manolis and Song, Shuran and Su, Hao and Xiao, Jianxiong and Yi, Li and Yu, Fisher},
  number      = {arXiv:1512.03012 [cs.GR]},
  institution = {Stanford University --- Princeton University --- Toyota Technological Institute at Chicago},
  year        = {2015}



If you use the ShapeNetSem dataset, please also cite the following publication:

Semantically-Enriched 3D Models for Common-sense Knowledge
Manolis Savva, Angel X. Chang, Pat Hanrahan
CVPR 2015 FPIC Workshop


Competitions and challenges

SHREC'16 Track Large-Scale 3D Shape Retrieval from ShapeNet Core55
Manolis Savva, Fisher Yu, Hao Su, Masaki Aono, Baoquan Chen, Daniel Cohen-Or, Weihong Deng, Hang Su, Song Bai, Xiang Bai, Noa Fish, Jiajie Han, Evangelos Kalogerakis, Erik G. Learned-Miller, Yangyan Li, Minghui Liao, Subhransu Maji, Atsushi Tatsuma, Yida Wang, Nanhai Zhang, Zhichao Zhou
Eurographics Workshop on 3D Object Retrieval 2016

SHREC'17 Track Large-Scale 3D Shape Retrieval from ShapeNet Core55
Manolis Savva, Fisher Yu, Hao Su, Asako Kanezaki, Takahiko Furuya, Ryutarou Ohbuchi, Zhichao Zhou5, Rui Yu, Song Bai, Xiang Bai, Masaki Aono, Atsushi Tatsuma, S. Thermos, A. Axenopoulos, G. Th. Papadopoulos, P. Daras, Xiao Deng, Zhouhui Lian, Bo Li, Henry Johan, Yijuan Lu, Sanjeev Mk
Eurographics Workshop on 3D Object Retrieval 2017


Datasets linking to or augmenting ShapeNet

ObjectNet3D: A Large Scale Database for 3D Object Recognition
ObjectNet3D links 3D shapes to images
Yu Xiang, Wonhui Kim, Wei Chen, Jingwei Ji, Christopher Choy, Hao Su, Roozbeh Mottaghi, Leonidas Guibas and Silvio Savarese
ECCV 2016

A Scalable Active Framework for Region Annotation in 3D Shape Collections
Part annotations for more than 30,000 models in 16 shape categories in ShapeNetCore
Li Yi, Vladimir G. Kim, Duygu Ceylan, I-Chao Shen, Mengyuan Yan, Hao Su, Cewu Lu, Qixing Huang, Alla Sheffer, Leonidas Guibas
SIGGRAPH Asia 2016

Shape Completion using 3D-Encoder-Predictor CNNs and Shape Synthesis
Virtual partial scans of ShapeNetCore models
Angela Dai, Charles Ruizhongtai Qi, Matthias Nießner
CVPR 2017

Cross-modal Attribute Transfer for Rescaling 3D Models
Size estimates for ShapeNetCore models
Lin Shao, Angel X. Chang, Hao Su, Manolis Savva, Leonidas Guibas
Proceedings of 3DV 2017

Other papers using or expanding ShapeNet

3D-Assisted Image Feature Synthesis for Novel Views of an Object
Hao Su*, Fan Wang*, Li Yi, Leonidas J. Guibas (* equal contribution)
ICCV 2015

Render for CNN: Viewpoint Estimation in Images Using CNNs Trained with Rendered 3D Model Views
Hao Su*, Charles R. Qi*, Yangyan Li, Leonidas J. Guibas (* equal contribution)
ICCV 2015

Joint Embeddings of Shapes and Images via CNN Image Purification
Yangyan Li*, Hao Su*, Charles R. Qi, Noa Fish, Daniel Cohen-Or, Leonidas J. Guibas (* joint first authors)
SIGGRAPH Asia 2015

Database-Assisted Object Retrieval for Real-Time 3D Reconstruction
Yangyan Li, Angela Dai, Leonidas Guibas, Matthias Nießner
Eurographics 2015

Shape Completion using 3D-Encoder-Predictor CNNs and Shape Synthesis
Angela Dai, Charles Ruizhongtai Qi, Matthias Nießner
CVPR 2017

Learning Hierarchical Shape Segmentation and Labeling from Online Repositories
Li Yi, Leonidas Guibas, Aaron Hertzmann, Vladimir G. Kim, Hao Su, Ersin Yumer

Synthesizing 3D shapes via modeling multi-view depth maps and silhouettes with deep generative networks
Amir Arsalan Soltani, Haibin Huang, Jiajun Wu, Tejas D Kulkarni, Joshua B. Tenenbaum
CVPR 2017

GRASS: Generative Recursive Autoencoders for Shape Structures
Jun Li, Kai Xu, Siddhartha Chaudhuri, Ersin Yumer, Hao Zhang, Leonidas Guibas

3D Semantic Segmentation with Submanifold Sparse Convolutional Networks
Segmentation for the randomly-rotated ICCV 2017 ShapeNet Competition dataset using fully-convolutional and U-Net style networks.
Benjamin Graham, Martin Engelcke, Laurens van der Maaten

Automatic 3D Car Model Alignment for Mixed Image-Based Rendering
Rodrigo Ortiz-Cayon, Abdelaziz Djelouah, Francisco Massa, Mathieu Aubry, George Drettakis
Proceedings of the International Conference on 3D Vision (3DV) 2016

High Resolution Shape Completion Using Deep Neural Networks for Global Structure and Local Geometry Inference
Xiaoguang Han, Zhen Li, Haibin Huang, Evangelos Kalogerakis, Yizhou Yu
Proceedings of the International Conference on Computer Vision (ICCV) 2017

Learning Local Shape Descriptors from Part Correspondences With Multi-view Convolutional Networks
Haibin Huang, Evangelos Kalogerakis, Siddhartha Chaudhuri, Duygu Ceylan, Vladimir Kim, Ersin Yumer
ACM Transactions on Graphics (to appear, also to be presented in SIGGRAPH 2018)

3D Shape Reconstruction from Sketches via Multi-view Convolutional Networks
Zhaoliang Lun, Matheus Gadelha, Evangelos Kalogerakis, Subhransu Maji, Rui Wang
Proceedings of the International Conference on 3D Vision (3DV) 2017

3D Shape Segmentation with Projective Convolutional Networks
Evangelos Kalogerakis, Melinos Averkiou, Subhransu Maji, Siddhartha Chaudhuri
Proceedings of the Computer Vision and Pattern Recognition (CVPR) 2017

Perspective Transformer Nets: Learning Single-View 3D Object Reconstruction without 3D supervision
Learning to reconstruct 3D voxels from images without explicit 3D voxel supervision.
Xinchen Yan, Jimei Yang, Ersin Yumer, Yijie Guo, Honglak Lee

O-CNN: Octree-based Convolutional Neural Networks for 3D Shape Analysis
Peng-Shuai Wang, Yang Liu, Yu-Xiao Guo, Chun-Yu Sun and Xin Tong

Probabilistic Structure From Motion With Objects (PSfMO)
Paul Gay, Cosimo Rubino, Vaibhav Bansal, Alessio Del Bue
ICCV 2017

If you use data or results associated with any of the above papers, please also cite them in addition to the main tech report. Please also let us know if you have a paper using ShapeNet that you would like us to list here.