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Research Statement
- Computer vision.
- Shape Analysis.
- Machine learning methods.
- Large-scale optimization.
Recent Publications
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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.
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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).
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Minhyuk Sung, Hao Su, Ronald Yu, and Leonidas Guibas, Deep Functional Dictionaries: Learning Consistent Semantic Structures on 3D Models from Functions, NeurIPS 2018
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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.
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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.
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Minhyuk Sung, Hao Su, Vladimir G. Kim, Siddhartha Chaudhuri, and Leonidas Guibas, ComplementMe: Weakly-Supervised Component Suggestions for 3D Modeling, SIGGRAPH Asia 2017.
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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.
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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.
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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).
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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)
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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
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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)
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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.
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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
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SU, H., WANG, F., YI, L., AND GUIBAS, L. 2015. 3D-Assisted Image Feature Synthesis for Novel Views of an Object. In ICCV IEEE.
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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).
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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.
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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).
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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
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