简介
王鹤博士,现任北京大学前沿计算研究中心助理教授,博士生导师,于2021年9月正式加入中心。他创立并领导了具身感知与交互实验室(EPIC Lab),实验室立足三维视觉感知与机器人学,重点关注具身机器人在三维复杂环境中的感知和交互问题,研究目标是以可扩增地方式发展高泛化性的机器人视觉和控制系统。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/NeurIPS)发表20余篇工作,其论文获得2022年世界人工智能大会青年优秀论文奖(WAICYOP),Eurographics 2019最佳论文提名奖,其带领的团队获得ICLR 2021可泛化机器人物体操纵挑战赛ManiSkill无额外标注赛道冠军。他担任了CVPR2022的领域主席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
Published Works
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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 |