Hao Su

haosu at stanford dot edu
personal page

Research interests

  • Computer vision.
  • Shape Analysis.
  • Machine learning methods.
  • Large-scale optimization.

Recent publications

(All the publications are available on the personal page linked above.)
C.R. Qi, W. Liu, C. Wu, H. Su, L.J. Guibas, Frustum PointNets for 3D Object Detection From RGB-D Data, IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2018.   
J. Huang, J. Gao, V. Ganapathi-Subramanian, H. Su, Y. Liu, C. Tang, and L. Guibas, DeepPrimitive: Image decomposition by layered primitive detection, Computational Visual Media 4(4): 385-397 (2018).   
Minhyuk Sung, Hao Su, Ronald Yu, and Leonidas Guibas, Deep Functional Dictionaries: Learning Consistent Semantic Structures on 3D Models from Functions, NeurIPS 2018   
S. Tulsiani, H. Su, L.J. Guibas, A.A. Efros, J. Malik, Learning Shape Abstractions by Assembling Volumetric Primitives, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017.   
L. Shao, A.X. Chang, H. Su, M. Savva, and L. Guibas, Cross-Modal Attribute Transfer for Rescaling 3D Models, International Conference on 3D Vision (3DV), 2017.   
Minhyuk Sung, Hao Su, Vladimir G. Kim, Siddhartha Chaudhuri, and Leonidas Guibas, ComplementMe: Weakly-Supervised Component Suggestions for 3D Modeling, SIGGRAPH Asia 2017.   
Charles R. Qi, Li Yi, Hao Su, and Leonidas J. Guibas. PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space. Neural Information Processing Systems (NIPS) 2017.   
Charles R. Qi, Hao Su, Kaichun Mo, and Leonidas J. Guibas. PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2017, Honolulu USA.   
T. Y. Wang, H. Su, Q. Huang, J. Huang, L. Guibas, N. Mitra, Unsupervised Texture Transfer from Images to Model Collections, ACM Transactions of Graphics (Proc. Siggraph Asia), 35(6), (2016).   
Li Yi, Vladimir G. Kim, Duygu Ceylan, I-Chao Shen, Mengyan Yan, Hao Su, Cewu Lu, Qixing Huang, Alla Sheffer, Leonidas J. Guibas, A scalable active framework for region annotation in 3D shape collections. ACM Trans. Graph. 35(6): 210 (2016)   
M. Savva, F. Yu, Hao Su, M. Aono, B. Chen, D. Cohen-Or, W. Deng, H. Su, S. Bai, X. Bai, N. Fish, J. Han, E. Kalogerakis, E. G. Learned-Miller, Y. Li, M. Liao, S. Maji, A. Tatsuma, Y. Wang, N. Zhang, Z. Zhou, SHREC’16 Track: Large-Scale 3D Shape Retrieval from ShapeNet Core55, EuroGraphics SHREC2016 Workshop Report   
Y. Li, S. Pirk, H. Su, C. R. Qi, L. J. Guibas, FPNN: Field Probing Neural Networks for 3D Data, Neural Information Processing Systems (NIPS 2016)   
Charles R. Qi, Hao Su, Matthias Nießner, Angela Dai, Mengyuan Yan, and Leonidas J. Guibas, Volumetric and Multi-View CNNs for Object Classification on 3D Data, IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2016, Las Vegas USA.   
Yu Xiang, Wonhui Kim, Wei Chen, Jingwei Ji, Christopher Bongsoo Choy, Hao Su, Roozbeh Mottaghi, Leonidas J. Guibas, Silvio Savarese: ObjectNet3D: A Large Scale Database for 3D Object Recognition. ECCV (8) 2016: 160-176   
SU, H., WANG, F., YI, L., AND GUIBAS, L. 2015. 3D-Assisted Image Feature Synthesis for Novel Views of an Object. In ICCV IEEE.   
H. Su, Q. Huang and N.J. Mitra,Y. Li and L. Guibas, Estimating image depth using shape collections, Transactions on Graphics (Special issue of SIGGRAPH 2014).   
Hao Su, Charles R. Qi, Yangyan Li and Leonidas J. Guibas, Render for CNN: Viewpoint Estimation in Images Using CNNs Trained with Rendered 3D Model Views, The 15th International Conference on Computer Vision (ICCV), Santiago, Chile, November 2015.   
Yangyan Li, Hao Su, Charles R. Qi, Noa Fish, Daniel Cohen-Or, and Leonidas J. Guibas, Joint Embeddings of Shapes and Images via CNN Image Purification, Transactions on Graphics (Special issue of SIGGRAPH Asia 2015).   
Kaichun Mo, Shilin Zhu, Angel Chang, Li Yi, Subarna Tripathi, Leonidas Guibas, and Hao Su, PartNet: A Large-scale Benchmark for Fine-grained and Hierarchical Part-level 3D Object Understanding, CVPR 2019