I'm a second-year CSE Ph.D. student at the University of Michigan, advised by David Fouhey. I obtained my bachelor degree from both the University of Michigan and Shanghai Jiao Tong University. During my undergraduate study at Michigan, I worked with Jia Deng in Vision & Learning Lab.

My primary research interest lies in Computer Vision, espeically 3D vision and recognition. I've closely collaborated with Weifeng Chen. I'm proud to work with my labmates in Fouhey AI Lab (FAIL) and the computer vision group @ Michigan.


  • [2020/08] Associative3D is invited to present at ECCV 2020 Workshop Holistic Scene Structures for 3D Vision.
  • [2020/07] "Associative3D: Volumetric Reconstruction from Sparse Views" is accepted at ECCV 2020!
  • [2020/02] "OASIS: A Large-Scale Dataset for Single-Image 3D in the Wild" is accepted at CVPR 2020!


Surface Normal

My research focuses on 3D vision, i.e. how do we recover the 3D world from a 2D image? I'm particularly interested in recovering 3D representations from Internet videos and computer graphics. Images above demonstrate different 3D representations we extract from 2D image(s) in my research projects.


Instructional Aide / Teaching Assistant:


Associative3D: Volumetric Reconstruction from Sparse Views.
Shengyi Qian*, Linyi Jin*, David Fouhey.
ECCV 2020.

We present Associative3D, which addresses 3D volumetric reconstruction from two views of a scene with an unknown camera, by simultaneously reconstructing objects and figuring out their relationship.

[eccv talk] [slides]

Invited presentation at ECCV 2020 Workshop Holistic Scene Structures for 3D Vision.
OASIS: A Large-Scale Dataset for Single-Image 3D in the Wild.
Weifeng Chen, Shengyi Qian, David Fan, Noriyuki Kojima, Max Hamilton, Jia Deng.
CVPR 2020.

We present Open Annotations of Single Image Surfaces (OASIS), a dataset for single-image 3D in the wild consisting of dense annotations of detailed 3D geometry for Internet images.

Learning Single-Image Depth from Videos using Quality Assessment Networks.
Weifeng Chen, Shengyi Qian, Jia Deng.
CVPR 2019.

We propose a method to automatically generate training data for single-view depth through Structure-from-Motion (SfM) on Internet videos.