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Chen Yu    

[ Ph.D. Candidate | NUS ]     [ Research Interests: 3DGS, NeRF, SfM, 3D Generation, SLAM ]

I obtained my doctoral degree at the National University of Singapore (NUS) under the guidance of Prof. Gim Hee Lee, supported by a Google PhD Fellowship. My academic journey began with an M.S. in Computer Science at Peking University, working with Prof. Yisong Chen and Prof. Shuhan Shen, and a B.S. in Software Engineering from Beihang University. I'm actively working on neural reconstruction, vision-language models, visual localization, etc.

NEWS  

  • [23.09.2025] I successfully defended my PhD thesis! Congratulation to me!
  • [19.09.2025] Our paper on exploring 3D geometric foundation models for Gaussian Splatting accepted at NeurIPS 2025!
  • [08.01.2025] I received the Research Achievement Award at SoC | NUS!
  • [30.09.2024] I received the Chinese Government Award for Outstanding Self-Financed Students Abroad!
  • [26.09.2024] Our paper on distributed large-scale 3D Gaussian Splatting accepted at NeurIPS 2024!
  • [29.05.2024] I was invited to give a talk at 3D CVer (3D视觉工坊) on our distributed 3D Gaussian Splatting !
  • [14.02.2024] Our paper on anti-aliasing 3D Gaussian Splatting accepted at CVPR 2024!
  • [11.01.2024] I am awarded the Dean's Graduate Research Achievement Award at NUS!
  • [03.11.2023] I am awarded the Google PhD Fellowship 2023 in the Machine Perception area!
  • [15.08.2023] I received the Research Achievement Award at SoC | NUS!
  • [14.07.2023] Our paper on registering neural radiance fields accepted at ICCV 2023!
  • [28.01.2023] Our paper on neural radiance fields accepted by CVPR 2023!
  • [17.01.2023] Our paper on large scale Structure-from-Motion accepted by ICRA 2023!
  • [10.01.2022] I joined the CVRP lab of SoC, NUS as a phd student!
  • [23.05.2021] Our paper on rotation averaging got accepted at CVPR 2021!
  • [08.07.2020] I joined Segway Robotics to work as a SLAM engineer!
  • [02.07.2020] Our paper on large-scale SfM got accepted at Pattern Recognition 2020!
  • [20.06.2020] I successfully defended my M.S. thesis on distributed large-scale Structure-from-Motion!
  • [15.11.2019] We won the 2nd place in the 3D Reconstruction Track of the 2nd China Virtual Reality and Application Innovation Challenge
  • [20.04.2019] I joined the rising self-driving startup TuSimple as a research intern on the group of High-Definition Localization and Mapping!
  • [05.09.2018] I joined Megvii Beijing as a algorithm intern on the group of SLAM!

PUBLICATIONS

Hover over the to get the rough idea
DOGS

Large-Scale Neural 3D Scene Reconstruction, Rendering, and Beyond

Yu Chen

PhD Thesis

Paper Slides   Cite
DOGS

Deep Gaussian from Motion: Exploring 3D Geometric Foundation Models for Gaussian Splatting

Yu Chen, Rolandos Alexandros Potamias, Evangelos Ververas, Jifei Song, Jiankang Deng, Gim Hee Lee

Neural Information Processing Systems (NeurIPS) 2025

Paper Web Page   Code   Cite
DOGS

DOGS: Distributed-Oriented Gaussian Splatting for Large-Scale 3D Reconstruction Via Gaussian Consensus

Yu Chen, Gim Hee Lee

Neural Information Processing Systems (NeurIPS) 2024

Paper Web Page   Code   Poster   Cite 
              @article{DBLP:journals/corr/abs-2405-13943,
                author       = {Yu Chen and
                                Gim Hee Lee},
                title        = {DoGaussian: Distributed-Oriented Gaussian Splatting for Large-Scale
                                3D Reconstruction Via Gaussian Consensus},
                journal      = {CoRR},
                volume       = {abs/2405.13943},
                year         = {2024},
                eprinttype    = {arXiv},
                eprint       = {2405.13943},
              }
MS-GS

Multi-Scale 3D Gaussian Splatting for Anti-Aliased Rendering

Zhiwen Yan, Weng Fei Low, Yu Chen, Gim Hee Lee

The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2024

Paper Web Page   Code   Cite 
              @inproceedings{DBLP:conf/cvpr/YanLCL24,
                author       = {Zhiwen Yan and
                                Weng Fei Low and
                                Yu Chen and
                                Gim Hee Lee},
                title        = {Multi-Scale 3D Gaussian Splatting for Anti-Aliased Rendering},
                booktitle    = {{IEEE/CVF} Conference on Computer Vision and Pattern Recognition},
                pages        = {20923--20931},
                publisher    = {{IEEE}},
                year         = {2024},
              }
DReg-NeRF

DReg-NeRF: Deep Registration for Neural Radiance Fields

Given NeRF blocks that are in different coordinate systems, DReg-NeRF registers them together end-to-end. Firstly, we download 1.8K+ 3D meshes from Objaverse and render meshes into images to train our NeRF model. Subsequently, DReg-NeRF extracts the occupancy grid from each NeRF block and feeds them into a 3D FPN and transformer to regress correspondences.
Yu Chen, Gim Hee Lee

The International Conference on Computer Vision (ICCV) 2023

Paper Web Page   Code   Poster   Data   Cite 
@inproceedings{DBLP:conf/iccv/ChenL23,
              author       = {Yu Chen and
                              Gim Hee Lee},
              title        = {DReg-NeRF: Deep Registration for Neural Radiance Fields},
              booktitle    = {{IEEE/CVF} International Conference on Computer Vision, {ICCV} 2023},
              pages        = {22646--22656},
              publisher    = {{IEEE}},
              year         = {2023},
            }
DBARF

DBARF: Deep Bundle-Adjusting Generalizable Neural Radiance Fields

DBARF jointly optimizes a generalizable neural radiance fields and the relative camera poses without pose initialization. We extract feature maps from nearby views and construct a residual map based on the extracted features. The residual map and rendering loss are minimized during training.
Yu Chen, Gim Hee Lee

The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2023

Paper Web Page   Code   Poster   Cite 
@InProceedings{Chen_2023_CVPR,
                  author    = {Chen, Yu and Lee, Gim Hee},
                  title     = {DBARF: Deep Bundle-Adjusting Generalizable Neural Radiance Fields},
                  booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
                  month     = {June},
                  year      = {2023},
                  pages     = {24-34}
              }
AdaSfM

AdaSfM: From Coarse Global to Fine Incremental Adaptive Structure from Motion

AdaSfM aims at adaptively recovering camera poses and scene structures in very large-scale outdoor scenes. We adopt a coarse-to-fine strategy for SfM. In AdaSfM, a coarse global SfM is reconstructed and then used to provide priors to the local incremental SfM reconstruction. The local incremental SfM can be run in a distributed way.
Yu Chen, Zihao Yu, Shu Song, Tianning Yu, Jianming Li, Gim Hee Lee

The IEEE International Conference on Robotics and Automation (ICRA) 2023

Paper Web Page   Code   Poster   Cite 
@inproceedings{DBLP:conf/icra/ChenYSYLL23,
                  author       = {Yu Chen and
                                  Zihao Yu and
                                  Shu Song and
                                  Tianning Yu and
                                  Jianming Li and
                                  Gim Hee Lee},
                  title        = {AdaSfM: From Coarse Global to Fine Incremental Adaptive Structure
                                  from Motion},
                  booktitle    = {{IEEE} International Conference on Robotics and Automation},
                  pages        = {2054--2061},
                  publisher    = {{IEEE}},
                  year         = {2023},
                }
global RA

Hybrid Rotation Averaging: A Fast and Robust Rotation Averaging Approach

Hybrid rotation averaging hybrids a global rotation averaging method and a local rotation averaging method. The robustness is enhanced by pre-filtering wrong edges in the triplets. The resulted global rotations are used as priors to regularize the camera poses in incremental SfM.
Yu Chen, Ji Zhao, Laurent Kneip

The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2021

Paper Web Page   Code   Cite 
@inproceedings{DBLP:conf/cvpr/Chen0K21,
                    author    = {Yu Chen and
                      Ji Zhao and
                      Laurent Kneip},
                      title     = {Hybrid Rotation Averaging: {A} Fast and Robust Rotation Averaging
                      Approach},
                      booktitle = {{IEEE} Conference on Computer Vision and Pattern Recognition},
                      pages     = {10358--10367},
                      year      = {2021},
                }
global RA

Graph-Based Parallel Large Scale Structure from Motion

GraphSfM splits and stitches scenes based on the graph connectivity. GraphSfM can reconstruct large-scale scenes efficiently in a distributed manner. We made the project publicly available and provide the first open-source distributed SfM implementation.
Yu Chen, Shuhan Shen, Yisong Chen, Guoping Wang

Pattern Recognition (PR) 2020

Paper Web Page   Code   Cite 
@article{DBLP:journals/pr/ChenSCW20,
                author    = {Yu Chen and
                            Shuhan Shen and
                            Yisong Chen and
                            Guoping Wang},
                title     = {Graph-based parallel large scale structure from motion},
                journal   = {Pattern Recognition},
                volume    = {107},
                pages     = {107537},
                year      = {2020},
              }

AWARDS  

EXPERIENCES

Segway Robotics

, July. 2020 - Dec. 2021
SLAM Algorithm Engineer (with an excellent team)
Visual-Inertial-Odometry SLAM, Marker SLAM, Outdoor Multi-sensor Fusion SfM

Segway Robotics

, Apr. 2020 - Jun. 2020
Algorithm Intern (with Jianming Li)
Visual-Inertial SLAM, Self-Calibration on scooters

TuSimple

, Apr. 2019 - Jul. 2019
Research Intern (with Dr. Ji Zhao)
High-Definition mapping, Rotation Averaging

Megvii Research

, Sep. 2018 - Dec. 2018
Algorithm Intern
Real-time 3D reconstruction, texture mapping