Linyi Jin

I am a third-year Ph.D. student at the University of Michigan, advised by Prof. David Fouhey. I work on computer vision and machine learning.

My research is related to 3D scene understanding, camera calibration and robotics. I was previously a Master student in Robotics. Before that, I received my B.S.E. degrees in Computer Science at UM and Mechanical Engineering at Shanghai Jiao Tong University through a dual degree program at UM-SJTU Joint Institute.

I am looking for internship position in 2024. Please feel free to contact me if you think I’d be a good match!

Email  /  CV  /  Google Scholar  /  Github

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- [2023/09] I will be a visiting student at since David is moving there!
- [2023/06] We have released demo for PerspectiveFields. Try it out on your camera calibration problem!
- [2023/03] Perspective Fields for Single Image Camera Calibration is selected as a highlight!
- [2023/02] Two papers are accepted at CVPR 2023!

Work Experience

Adobe Research
Computer Vision Research Intern
Summer, 2021
Host: Jianming Zhang


Perspective Fields for Single Image Camera Calibration.
Linyi Jin, Jianming Zhang, Yannick Hold-Geoffroy, Oliver Wang, Kevin Matzen, Matthew Sticha, David Fouhey
CVPR, 2023 (Highlight -- 2.5% accept rate)
project page / demo / arXiv / code / bibtex

A novel image space representation for camera perspectives, facilitating precise calibration in in-the-wild environments and cropped images.

Learning to Predict Scene-Level Implicit 3D from Posed RGBD Data.
Nilesh Kulkarni, Linyi Jin, Justin Johnson, David Fouhey
CVPR, 2023
project page / arXiv / code / bibtex

Learning 3D implicit function from a single input image. Unlike other methods, D2-DRDF does not depend on mesh supervision during training and can directly operate with raw RGB-D data obtained from scene captures.

PlaneFormers: From Sparse View Planes to 3D Reconstruction.
Samir Agarwala, Linyi Jin, Chris Rockwell, David Fouhey
ECCV, 2022
project page / arXiv / code / bibtex

We introduce a simpler approach that uses a transformer applied to 3D-aware plane tokens to perform 3D reasoning. This is substantially more effective than SparsePlanes.

Understanding 3D Object Articulation in Internet Videos.
Shengyi Qian, Linyi Jin, Chris Rockwell, Siyi Chen, David Fouhey
CVPR, 2022
project page / arXiv / code / bibtex

We propose to investigate detecting and characterizing the 3D planar articulation of objects from ordinary videos.

SparsePlanes: Planar Surface Reconstruction from Sparse Views.
Linyi Jin, Shengyi Qian, Andrew Owens, David Fouhey
ICCV, 2021 (Oral)
project page / arXiv / code / bibtex

We learn to reconstruct scenes from sparse views with an unknown relationship. We take advantage of planar regions and their geometric properties to recover the scene layout.

Associative3D: Volumetric Reconstruction from Sparse Views.
Shengyi Qian*, Linyi Jin*, David Fouhey
ECCV, 2020
project page / arXiv / code / bibtex

We can build a voxel-based reconstruction of images from two views, even without access to the relative camera positions.

Invited presentation at ECCV 2020 Workshop Holistic Scene Structures for 3D Vision.

Inferring Occluded Geometry Improves Performance when Retrieving an Object from Dense Clutter.
Andrew Price*, Linyi Jin*, Dmitry Berenson
ISRR, 2019
project page / arXiv / bibtex

We augment a manipulation planner for cluttered environments with a shape completion network and a volumetric memory system, allowing the robot to reason about what may be contained in occluded areas.


EECS 442 Computer Vision (Winter '19)
IA with David Fouhey.

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