Yijun Yuan (元祎君)

• I’m now a PhD student in Würzburg University. My supervisers are Prof. Dr. Andreas Nüchter, Prof. Dr. Radu Timofte and Prof. Dr. Sören Schwertfeger (External). Before that I received my Bachelor’s and Master’s Degree from dear ShanghaiTech University.
• I’m interested in Robotics. My previous works touch upon Rescue Robotics, Registration and Mapping.

CV | ResearchGate | codes

News

• Starting my PhD research visiting in Prof. Marc Pollefeys’s group in ETH Zürich (Jan, 2024)

• “Uni-Fusion: Universal Continuous Mapping” was accepted to T-RO 2024 (December, 2023)

• “Online Learning of Neural Surface Light Fields alongside Real-time Incremental 3D Reconstruction” was accepted to RAL 2023 (April, 2023)

• “An Algorithm for the SE(3)-Transformation on Neural Implicit Maps for Remapping Functions” was accepted to RAL 2022 (June, 2022)

• “Indirect Point Cloud Registration: Aligning Distance Fields using a Pseudo Third Point Set” was accepted to RAL 2022 (May, 2022)

• A new start in Wuerzburg University (15 sept, 2021. )

Recent researches

Uni-Fusion: Universal Continuous Mapping Yijun Yuan, Andreas Nuchter IEEE Transactions on Robotics (T-RO), 2024 website | paper | code The first universal continuous mapping framework for surfaces, surface properties (color, infrared, style, saliency, etc.) and more (latent features in CLIP embedding space, etc.).

Online Learning of Neural Surface Light Fields alongside Real-time Incremental 3D Reconstruction Yijun Yuan, Andreas Nuchter IEEE Robotics and Automation Letters (RAL), 2023 website | paper | code NSLF-OL explores more fully decoupling the representation of geometry and color. Working alongside real-time surface reconstruction, NSLF-OL focuses on surface field to provide efficient online modeling of light.

An Algorithm for the SE(3)-Transformation on Neural Implicit Maps for Remapping Functions Yijun Yuan, Andreas Nuchter IEEE Robotics and Automation Letters (RAL), 2022 website | paper | code SE(3)-transformation on neural-implicit maps and enable remapping function. This is the first algorithm that make neural-implicit based reconstruction compatible with Loop-closure.

Indirect Point Cloud Registration: Aligning Distance Fields using a Pseudo Third Point Set Yijun Yuan, Andreas Nuchter IEEE Robotics and Automation Letters (RAL), 2022 paper | code Registration two point cloud with a pseudo-third point set. Thus method have potential to work with neural-implicit registration.

Full publications show in page. All codes have been open-released.