+86 (0)10 6276-1084
hewang
Room 106-1, Courtyard No.5, Jingyuan
Embodied AI, 3D Computer Vision, Robotics https://hughw19.github.io/Bio-Sketch
Dr. He Wang joined Peking University in September 2021 and is currently a tenure-track assistant professor at the Center on Frontiers of Computing Studies (CFCS) at Peking University, where he founds and leads Embodied Perception and InteraCtion (EPIC) Lab. His research interests span 3D vision, robotics, and machine learning, with a special focus on embodied AI. His research objective is to endow robots working in complex real-world scenes with generalizable 3D vision and interaction policies in a scalable way. He has published more than 20 papers on top conferences and journals of computer vision, robotics and learning (CVPR/ICCV/ECCV/TRO/RAL/NeurIPS) with 8 of his works receiving CVPR/ICCV orals, one work receiving 2022 World Artificial Intelligence Conference Youth Outstanding Paper (WAICYOP) Award, and one work receiving Eurographics 2019 best paper honorable mention. He serves as an area chair in CVPR 2022. Prior to joining Peking University, he received his Ph.D. degree from Stanford University in 2021 under the advisory of Prof. Leonidas J. Guibas and his Bachelor's degree in 2014 from Tsinghua University.
Publications
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 |