Shuyi Zhou   周舒意ゾウ シュウイ

Ph.D. candidate @ the University of Tokyo · Member of Oishi Lab · Research interests: 3D vision, LiDAR–camera fusion

zhoushuyi495@gmail.com GitHub CV

I am a Ph.D. candidate at the The University of Tokyo, advised by Prof. Takeshi Oishi. I received my Master's degree from The University of Tokyo in 2023 and my Bachelor of Science degree (Electrical and Computer Engineering) from Shanghai Jiao Tong University (UM-SJTU Joint Institute) in 2021.

My research focuses on 3D vision, particularly sensor fusion and differentiable rendering techniques such as Gaussian Splatting and Neural Radiance Fields (NeRF). I excel at developing innovative algorithms that enhance 3D reconstruction and scene understanding from multi-modal data sources.

I am looking for full-time job opportunities from Oct. 2026.

Publications

DeMapGS: Simultaneous Mesh Deformation and Surface Attribute Mapping via Gaussian Splatting

SIGGRAPH Asia 2025 | Shuyi Zhou, Shengze Zhong, Kenshi Takayama, Takafumi Taketomi, Takeshi Oishi

A structured Gaussian splatting framework enabling mesh deformation and editable attribute mapping.

Paper Project Video

Robust LiDAR-Camera Calibration with 2D Gaussian Splatting

Ra-L 2025 | Shuyi Zhou, Shuxiang Xie, Ryoichi Ishikawa, Takeshi Oishi

A targetless LiDAR-camera calibration method that consolidates various geometric constraints based on 2DGS.

Paper Project Video

G²fR: Frequency Regularization in Grid-based Feature Encoding Neural Radiance Fields

ECCV 2024 | Shuxiang Xie, Shuyi Zhou, Ken Sakurada, Ryoichi Ishikawa, Masaki Onishi, and Takeshi Oishi

Generalized frequency regularization method for grid-based implicit representations (e.g., Instant-NGP), analyzing their mathematical principles and frequency behavior.

Paper Video

LiDAR-Camera Calibration using Intensity Variance Cost

ICRA 2024 | Ryoichi Ishikawa, Shuyi Zhou, Yoshihiro Sato, Takeshi Oishi, Katsushi Ikeuchi

A targetless LiDAR-camera calibration applicable even to 1D LiDAR using the cost function based on intensity variance.

Paper

INF: Implicit Neural Fusion for LiDAR and Camera

IROS 2023 | Shuyi Zhou, Shuxiang Xie, Ryoichi Ishikawa, Ken Sakurada, Masaki Onishi, Takeshi Oishi

Implicit neural representations with diverse sensor inputs to achieve robust sensor fusion and calibration.

Paper Project Video

Experience

Awards