Su, Hao

ImageNet Large Scale Visual Recognition Challenge

Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al. (2015) · International Journal of Computer Vision

Advanced Neural Network ApplicationsAdvanced Image and Video Retrieval Techniques

Cited by 39,380

Frustum PointNets for 3D Object Detection from RGB-D Data

Qi, C. R., Liu, W., Wu, C., Su, H., & Guibas, L. J. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition

Advanced Neural Network Applications3D Shape Modeling and Analysis

Cited by 2,655

ShapeNet: An Information-Rich 3D Model Repository

Chang, A. X., Funkhouser, T., Guibas, L., Hanrahan, P., Huang, Q., Li, Z., Savarese, S., Savva, M., Song, S., Su, H., et al. (2015) · arXiv

3D Shape Modeling and AnalysisImage Processing and 3D Reconstruction

Cited by 2,330

ImageNet Large Scale Visual Recognition Challenge

Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al. (2014) · arXiv

Advanced Neural Network ApplicationsAdvanced Image and Video Retrieval Techniques

Cited by 567

SyncSpecCNN: Synchronized Spectral CNN for 3D Shape Segmentation

Yi, L., Su, H., Guo, X., & Guibas, L. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

3D Shape Modeling and AnalysisAdvanced Graph Neural Networks

Cited by 461

SAPIEN: A SimulAted Part-Based Interactive ENvironment

Xiang, F., Qin, Y., Mo, K., Xia, Y., Zhu, H., Liu, F., Liu, M., Jiang, H., Yuan, Y., Wang, H., et al. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Robot Manipulation and LearningReinforcement Learning in Robotics

Cited by 406

Deep Stereo Using Adaptive Thin Volume Representation With Uncertainty Awareness

Cheng, S., Xu, Z., Zhu, S., Li, Z., Li, L. E., Ramamoorthi, R., & Su, H. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Advanced Vision and Imaging3D Shape Modeling and Analysis

Cited by 385

Open X-Embodiment: Robotic Learning Datasets and RT-X Models : Open X-Embodiment Collaboration 0

O’Neill, A., Rehman, A., Maddukuri, A., Gupta, A., Padalkar, A., Lee, A., Pooley, A., Gupta, A., Mandlekar, A., Jain, A., et al. (2024) · 2024 IEEE International Conference on Robotics and Automation (ICRA)

Robot Manipulation and LearningReinforcement Learning in Robotics

Cited by 210

Binary Ensemble Neural Network: More Bits per Network or More Networks per Bit?

Zhu, S., Dong, X., & Su, H. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Advanced Neural Network ApplicationsNeural Networks and Applications

Cited by 153

Open X-Embodiment: Robotic Learning Datasets and RT-X Models

Embodiment Collaboration, O'Neill, A., Rehman, A., Gupta, A., Maddukuri, A., Gupta, A., Padalkar, A., Lee, A., Pooley, A., Gupta, A., et al. (2023) · arXiv

Robot Manipulation and LearningReinforcement Learning in Robotics

Cited by 111

Rearrangement: A Challenge for Embodied AI

Batra, D., Chang, A. X., Chernova, S., Davison, A. J., Deng, J., Koltun, V., Levine, S., Malik, J., Mordatch, I., Mottaghi, R., et al. (2020) · arXiv

Robot Manipulation and LearningReinforcement Learning in Robotics

Cited by 109

AnyTeleop: A General Vision-Based Dexterous Robot Arm-Hand Teleoperation System

Qin, Y., Yang, W., Huang, B., Wyk, K., Su, H., Wang, X., Chao, Y.-W., & Fox, D. (2023) · Robotics: Science and Systems XIX

Teleoperation and Haptic SystemsRobot Manipulation and Learning

Cited by 109

From One Hand to Multiple Hands: Imitation Learning for Dexterous Manipulation From Single-Camera Teleoperation

Qin, Y., Su, H., & Wang, X. (2022) · IEEE Robotics and Automation Letters

Robot Manipulation and LearningReinforcement Learning in Robotics

Cited by 104

CAPTRA: CAtegory-level Pose Tracking for Rigid and Articulated Objects from Point Clouds

Weng, Y., Wang, H., Zhou, Q., Qin, Y., Duan, Y., Fan, Q., Chen, B., Su, H., & Guibas, L. J. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV)

3D Shape Modeling and AnalysisRobot Manipulation and Learning

Cited by 103

Approximate convex decomposition for 3D meshes with collision-aware concavity and tree search

Wei, X., Liu, M., Ling, Z., & Su, H. (2022) · ACM Transactions on Graphics

3D Shape Modeling and AnalysisComputer Graphics and Visualization Techniques

Cited by 80

Temporal Difference Learning for Model Predictive Control

Hansen, N., Wang, X., & Su, H. (2022) · arXiv

Reinforcement Learning in RoboticsAdvanced Control Systems Optimization

Cited by 50

Deductive Verification of Chain-of-Thought Reasoning

Ling, Z., Fang, Y., Li, X., Huang, Z., Lee, M., Memisevic, R., & Su, H. (2023) · Advances in Neural Information Processing Systems 36

Logic, Reasoning, and KnowledgeLogic, programming, and type systems

Cited by 44

Deductive Verification of Chain-of-Thought Reasoning

Ling, Z., Fang, Y., Li, X., Huang, Z., Lee, M., Memisevic, R., & Su, H. (2023) · arXiv

Logic, Reasoning, and KnowledgeLogic, programming, and type systems

Cited by 44

ManiSkill: Generalizable Manipulation Skill Benchmark with Large-Scale Demonstrations

Mu, T., Ling, Z., Xiang, F., Yang, D., Li, X., Tao, S., Huang, Z., Jia, Z., & Su, H. (2021) · arXiv

Robot Manipulation and LearningReinforcement Learning in Robotics

Cited by 34

ManiSkill2: A Unified Benchmark for Generalizable Manipulation Skills

Gu, J., Xiang, F., Li, X., Ling, Z., Liu, X., Mu, T., Tang, Y., Tao, S., Wei, X., Yao, Y., et al. (2023) · arXiv

Robot Manipulation and LearningReinforcement Learning in Robotics

Cited by 23

RigAnything: Template-Free Autoregressive Rigging for Diverse 3D Assets

Liu, I., Xu, Z., Yifan, W., Tan, H., Xu, Z., Wang, X., Su, H., & Shi, Z. (2025) · ACM Transactions on Graphics

3D Shape Modeling and AnalysisHuman Motion and Animation

Cited by 12

Improving Policy Optimization with Generalist-Specialist Learning

Jia, Z., Li, X., Ling, Z., Liu, S., Wu, Y., & Su, H. (2022) · arXiv

Reinforcement Learning in RoboticsDomain Adaptation and Few-Shot Learning

Cited by 10

TD-MPC2: Scalable, Robust World Models for Continuous Control

Hansen, N., Su, H., & Wang, X. (2023) · arXiv

Reinforcement Learning in RoboticsAI-based Problem Solving and Planning

Cited by 8

Evaluating Real-World Robot Manipulation Policies in Simulation

Li, X., Hsu, K., Gu, J., Pertsch, K., Mees, O., Walke, H. R., Fu, C., Lunawat, I., Sieh, I., Kirmani, S., et al. (2024) · arXiv

Robot Manipulation and LearningReinforcement Learning in Robotics

Cited by 4