Hide Matsuki

I am currently pursuing my PhD at the Dyson Robotics Lab at Imperial College London, under the supervision of Prof. Andrew Davison.
During my PhD, I was a research intern at Google Zurich hosted by Federico Tombari and Keisuke Tateno.

Previously, I completed my Master's degree at the University of Tokyo. During my master's program, I had the opportunity to work at the Technische Universität München (TUM) with Prof. Daniel Cremers.

Between my master's and PhD degree, I worked as a computer vision engineer at Artisense, a startup co-founded by Prof. Cremers.

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profile photo


My research focuses on real-time computer vision and robotics, with a particular emphasis on visual SLAM.

I strongly believe all the SLAM innovations are made in the process of pursuing hard realtime operation and interactivity (e.g. tracking/mapping seperation, keyframing, scene representations, Gauss-Newton variants, tools like Sophus/Pangolin). To this end, I work on developing efficient algorithms and 3D representations for SLAM, tailored to real-world, real-time scenarios.


Gaussian Splatting SLAM
Hidenobu Matsuki*, Riku Murai*, Paul H.J. Kelly, Andrew J. Davison
IEEE / CVF Computer Vision and Pattern Recognition Conference (CVPR), 2024
project page / video / paper

The first Monocular SLAM using Gaussian Splatting as a master representation. The method also supports RGB-D mode.

PontTuset NEWTON: Neural View-Centric Mapping for On-the-Fly Large-Scale SLAM
Hidenobu Matsuki, Keisuke Tateno, Michael Niemeyer, Federico Tombari,
arXiv, 2023

Multiple local Neural Fields for efficiently handling loop closure in large-scale SLAM.

PontTuset iMODE:Real-Time Incremental Monocular Dense Mapping Using Neural Field
Hidenobu Matsuki, Edgar Sucar, Tristan Laidlow, Kentaro Wada, Raluca Scona, Andrew J. Davison
IEEE International Conference on Robotics and Automation (ICRA), 2023   (Oral Presentation, Best Navigation Paper Award Finalist)

Generic real-time monocular dense mapping pipeline utilizing the smoothness prior inhereint in Multi Layer Perceptron.

PontTuset CodeMapping: Real-Time Dense Mapping for Sparse SLAM using Compact Scene Representations
Hidenobu Matsuki, Raluca Scona, Jan Czarnowski, Andrew J. Davison,
IEEE Robotics and Automation Letters (RA-L), 2021
paper / video

Real-time dense mapping framework with compact scene representations from generataive model.

PontTuset Omniirectional DSO: Direct Sparse Odometry with Fisheye Cameras
Hidenobu Matsuki, Lukas von Stumberg, Vladyslav Usenko, Jörg Stückler, Daniel Cremers
IEEE Robotics and Automation Letters (RA-L), 2018
paper / video

Real-time direct visual odometry for omnidirectional cameras

Academic Service

  • Conference Reviewer: ICRA, IROS
  • Journal Reviewer: RA-L, T-RO, IJRR


I often use a photo of deer taken in Nara, Japan for my profile, as it's where I grew up and I also respect the groundbreaking PTAM demo connected with this place. Outside of research and work, I enjoy playing rugby on the muddy pitches of England.