简介
王鹤博士是北京大学前沿计算研究中心(CFCS)的助理教授和博士生导师。他创立并领导了北大具身感知与交互实验室(EPIC Lab,主页:https://hughw19.github.io),研究目标是通过发展具身技能及具身多模态大模型推进通用具身智能。他同时是北大-银河通用具身智能联合实验室主任,北京智源人工智能研究院具身智能研究中心主任。他已在计算机视觉、机器人学和人工智能的顶级会议和期刊(CVPR/ICCV/ECCV/TRO/RAL/ICRA/NeurIPS/ICLR/AAAI等)上发表五十余篇工作,其论文获得ICCV2023最佳论文候选,ICRA2023最佳操纵论文候选,2022年世界人工智能大会青年优秀论文(WAICYOP)奖,Eurographics 2019最佳论文提名奖。他担任了CVPR2022和WACV2022的领域主席,Image and Vision Computing的副主编和诸多顶会的审稿人、程序委员。在加入北京大学之前,他于2021年从斯坦福大学获得博士学位,师从美国三院院士Leonidas. J Guibas教授,于2014年从清华大学获得学士学位。
发表论著
Selected Publications
*: equivalent contribution, †: corresponding author
ASRO-DIO: Active Subspace Random Optimization Based Depth Inertial Odometry
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Domain Randomization-Enhanced Depth Simulation and Restoration for Perceiving and Grasping Specular and Transparent Objects
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Learning Category-Level Generalizable Object Manipulation Policy via Generative Adversarial Self-Imitation Learning from Demonstrations
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FisherMatch: Semi-Supervised Rotation Regression via Entropy-based Filtering
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Projective Manifold Gradient Layer for Deep Rotation Regression
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ADeLA: Automatic Dense Labeling with Attention for Viewpoint Adaptation in Semantic Segmentation
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HOI4D: A 4D Egocentric Dataset for Category-Level Human-Object Interaction
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Multi-Robot Active Mapping via Neural Bipartite Graph Matching
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CodedVTR: Codebook-based Sparse Voxel Transformer with Geometric Guidance
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Domain Adaptation on Point Clouds via Geometry-Aware Implicits
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Leveraging SE(3) Equivariance for Self-supervised Category-Level Object Pose Estimation from Point Clouds
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CAPTRA: CAtegory-level Pose Tracking for Rigid and Articulated Objects from Point Clouds
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Single Image 3D Shape Retrieval via Cross-Modal Instance and Category Contrastive Learning
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3DIoUMatch: Leveraging IoU Prediction for Semi-Supervised 3D Object Detection
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MultiBodySync: Multi-Body Segmentation and Motion Estimation via 3D Scan Synchronization
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Robust Neural Routing Through Space Partitions for Camera Relocalization in Dynamic Indoor Environments
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Rethinking Sampling in 3D Point Cloud Generative Adversarial Networks
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PT2PC: Learning to Generate 3D Point Cloud Shapes from Part Tree Conditions
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Curriculum DeepSDF
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Category-level Articulated Object Pose Estimation
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SAPIEN: A SimulAted Part-based Interactive ENvironment
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Normalized Object Coordinate Space for Category-Level 6D Object Pose and Size Estimation
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GSPN: Generative Shape Proposal Network for 3D Instance Segmentation in Point Cloud
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Learning a Generative Model for Multi-Step Human-Object Interactions from Videos |