Hanzhi CHEN
PhD Student
Technical University Munich
My research interest lies in understandting 3D scenes from an object-centric perspective.
By exploring the information disclosed by objects, ranging from geometry to high-level concepts,
my objective is to utilize all sorts of knowledge to enhance real-world applications,
particularly for human-robot interaction and spatial reasoning.
Brief Bio
I am a PhD student in the Smart Robotics Lab
of Technical University of Munich with
Prof. Stefan Leutenegger.
Previously, I received my M.Sc. degree in Robotics, Cognition, Intelligence with high distinction from
Technical University of Munich. Most of my research during master study
is done at
CAMP advised by
Dr. Benjamin Busam,
Dr. Fabian Manhardt and
Prof. Nassir Navab.
PUBLICATIONS
(* and † indicate equal first authorship or equal last authorship.)
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FuncGrasp: Learning Object-Centric Neural Grasp Functions from Single Annotated Example Object
Hanzhi Chen, Binbin Xu, Stefan Leutenegger, ICRA, 2024 project page / paper / video We propose a framework to infer continuous grasp functions of unseen objects using only one annotated example. |
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Anthropomorphic Grasping with Neural Object Shape Completion
Diego Hidalgo Carvajal*, Hanzhi Chen*, Gemma Bettelani, Jaesug Jung, Melissa Zavaglia, Laura Busse, Abdeldjallil Naceri, Stefan Leutenegger†, Sami Haddadin† RA-L, 2023 project page / paper / video We design an anthropomorphic grasping system capable of manipulating previously unseen objects using single-view visual input. |
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TexPose: Neural Texture Learning for Self-Supervised 6D Object Pose Estimation
Hanzhi Chen, Fabian Manhardt, Nassir Navab, Benjamin Busam CVPR, 2023 project page / paper / code We re-formulate self-supervised 6D object pose estimation as two sub-optimization problems on texture learning and pose learning. |
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Attention meets Geometry: Geometry Guided Spatial-Temporal Attention for Consistent
Self-Supervised Monocular Depth Estimation
Hanzhi Chen*, Daoyi Gao*, Patrick Ruhkamp, Nassir Navab, Benjamin Busam 3DV, 2021 project page / paper / code We propose a self-supervised monocular depth estimator providing temporally coherent reconstruction. |
WORKSHOPS
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1st Workshop on Urban Scene Modeling: Where Vision Meets Photogrammetry and Graphics
CVPR, 2024 workshop page |