My work in Spatial AI aims to develop the perceptual foundations for intelligent agents. I focus on frameworks that fuse real-time sensor measurements with data prior, often through advancements in object-level SLAM, to build a coherent understanding of the world's semantic, geometric, and temporal structure. This enables agents to perform sophisticated reasoning and interact effectively with their physical environment.

I am currently a research scientist at Google XR, working on Spatial AI.

I obtained my PhD at Imperial College London (PhD thesis) in 2022, supervised by Prof. Stefan Leutenegger and Prof. Andrew Davison. Follwoing that, I worked as a Postdoc fellow in the Autonomous Space Robotics Lab (ASRL) of University of Toronto with Prof. Tim Barfoot. I received my Master degree in Precision Engineering from the University of Tokyo in 2017, supervised by Prof. Atsushi Yamashita and Prof. Hajime Asama. I got my Bachelor's degree in Information Engineering from South China University of Technology in 2014.

Explainability-Aware Frustum Attack: Exposing Structural Vulnerabilities in LiDAR-Based 3D Object Detectors Chengzeng You, Binbin Xu, Soteris Demetriou European Conference on Computer Vision (ECCV), 2026 arXiv | video | code

UniScale: Unified Scale-Aware 3D Reconstruction for Multi-View Understanding via Prior Injection for Robotic Perception Mohammad Mahdavian*, Gordon Tan*, Binbin Xu, Yuan Ren, Dongfeng Bai, Bingbing Liu IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2026 arXiv

UnPose: Uncertainty-Guided Diffusion Priors for Zero-Shot Pose Estimation Zhaodong Jiang, Ashish Sinha, Tongtong Cao, Yuan Ren, Bingbing Liu, Binbin Xu Conference on Robot Learning (CoRL), 2025 project | OpenReview | arXiv

Hippo: Harnessing image-to-3d priors for model-free zero-shot 6d pose estimation Yibo Liu, Zhaodong Jiang, Binbin Xu, Guile Wu, Yuan Ren, Tongtong Cao, Bingbing Liu, Rui Heng Yang, Amir Rasouli, Jinjun Shan IEEE Robotics and Automation Letters, 2025 (ICRA 2026 presentation) project | paper | arXiv

Toward General Object-level Mapping from Sparse Views with 3D Diffusion Priors Ziwei Liao , Binbin Xu, Steven L. Waslander Conference on Robot Learning (CoRL), 2024 arXiv | OpenReview | code

Identifying Optimal Launch Sites of High-Altitude Latex-Balloons using Bayesian Optimisation for the Task of Station-Keeping Jack Saunders, Sajad Saeedi, Adam Hartshorne, Binbin Xu, Özgur Şimşek, Alan Hunter, Wenbin Li IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2024 project | paper | arXiv | video

NeRF-VO: Real-Time Sparse Visual Odometry with Neural Radiance Fields Jens Naumann, Binbin Xu, Stefan Leutenegger, Xingxing Zuo IEEE Robotics and Automation Letters, 2024 project | paper | arXiv | video | code

FuncGrasp: Learning Object-Centric Neural Grasp Functions from Single Annotated Example Object Hanzhi Chen, Binbin Xu, Stefan Leutenegger IEEE International Conference on Robotics and Automation (ICRA), 2024 project | paper | arXiv | video

Incremental Dense Reconstruction from Monocular Video with Guided Sparse Feature Volume Fusion Xingxing Zuo, Nan Yang, Nathaniel Merrill, Binbin Xu, Stefan Leutenegger IEEE Robotics and Automation Letters, 2023 paper | arXiv | video

Accurate and Interactive Visual-Inertial Sensor Calibration with Next-Best-View and Next-Best-Trajectory Suggestion Christopher L Choi, Binbin Xu, Stefan Leutenegger IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2023 project | paper | arXiv | video | code

What to learn: Features, image transformations, or both? Yuxuan Chen, Binbin Xu, Frederike Dümbgen, Timothy D Barfoot IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2023 paper | arXiv | video

Finding Things in the Unknown: Semantic Object-Centric Exploration with an MAV Sotiris Papatheodorou, Nils Funk, Dimos Tzoumanikas, Christopher Choi, Binbin Xu, Stefan Leutenegger IEEE International Conference on Robotics and Automation (ICRA), 2023 project | paper | arXiv | video | code

	Learning to Complete Object Shapes for Object-level Mapping in Dynamic Scenes Binbin Xu, Andrew J. Davison, Stefan Leutenegger IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2022 project | paper | arXiv | video
	Visual-Inertial Multi-Instance Dynamic SLAM with Object-level Relocalisation Yifei Ren*, Binbin Xu*, Christopher L Choi, Stefan Leutenegger IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2022 project | paper | arXiv | video | code
	Deep Probabilistic Feature-metric Tracking Binbin Xu, Andrew J. Davison, Stefan Leutenegger IEEE Robotics and Automation Letters (RA-L), Vol. 6, No. 1, pp. 223-230, 2021 (ICRA 2021 presentation) Honorable Mention of RA-L 2021 Best Paper Award paper | arXiv | video | code
	MID-Fusion: Octree-based Object-Level Multi-Instance Dynamic SLAM Binbin Xu, Wenbin Li, Dimos Tzoumanikas, Michael Bloesch, Andrew Davison, Stefan Leutenegger IEEE International Conference on Robotics and Automation (ICRA), 2019 paper | arXiv | video | rgb-jacobian | data | code
	Spatio-temporal Video Completion in Spherical Image Sequences Binbin Xu, Sarthak Pathak, Hiromitsu Fujii, Atsushi Yamashita and Hajime Asama IEEE Robotics and Automation Letters (RA-L), Vol. 2, No. 4, pp. 2032-2039, 2017 paper | video