Qimin Chen

I am a Ph.D. student in GrUVi lab of School of Computing Science at Simon Fraser University, under the supervision of Prof. Hao (Richard) Zhang.

Before that, I received my M.S. degree in Computational Science from UC San Diego, where I worked at Center for Visual Computing Lab advised by Prof. David J. Kriegman. I also worked at Machine Learning, Perception, and Cognition Lab (mlPC) advised by Prof. Zhuowen Tu. Before that, I received my B.S degree in Computer Science and Technology from Fuzhou University.

qca43 at sfu dot ca  /  CV  /  LinkedIn  /  Google Scholar  /  Github


mind map    AI Conference Deadlines
cvf    CVF Open Access
3d    3D Shape Analysis Paper List
          Neural Rendering Paper List
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Research

My research interest mainly focuses on Computer Vision/Graphics, especially geometric modeling as well as generative model for 3D shape and scene understanding and reconstruction. I am also interested in object detection and segmentation.

ShaDDR: Interactive Example-Based Geometry and Texture Generation via 3D Shape Detailization and Differentiable Rendering
Qimin Chen, Zhiqin Chen, Hang Zhou, Hao Zhang
SIGGRAPH Asia, 2023 (Conference)
pdf / supplementary / code / project page

The first example-based deep generative neural network for generating a high-resolution textured 3D shape through geometry detailization and conditional texture generation applied to an input coarse voxel shape.

D2CSG: Unsupervised Learning of Compact CSG Trees with Dual Complements and Dropouts
Fenggen Yu, Qimin Chen, Maham Tanveer, Ali Mahdavi-Amiri, Hao Zhang
NeurIPS, 2023
arXiv

D2CSG is a neural model composed of two dual and complementary network branches, with dropouts, for unsupervised learning of compact constructive solid geometry (CSG) representations of 3D CAD shapes.

UNIST: Unpaired Neural Implicit Shape Translation Network
Qimin Chen, Johannes Merz, Aditya Sanghi, Hooman Shayani, Ali Mahdavi-Amiri, Hao Zhang
CVPR, 2022
pdf / supplementary / code / project page

The first deep neural implicit model for general-purpose, unpaired shape-to-shape translation, in both 2D and 3D domains. UNIST can learn both style-preserving content alteration and content-preserving style transfer.

Topology-Aware Single-Image 3D Shape Reconstruction
Qimin Chen, Vincent Nguyen, Feng Han, Raimondas Kiveris, Zhuowen Tu
CVPR Workshop on Learning 3D Generative Models (CVPRW), 2020
pdf / poster / bibtex

Composing volumetric-based generative model with topology-awareness auto-encoder allows them to learn high-level topological properties such as genus and connectivity for 3D shape reconstruction.

A New Deep Learning Engine for CoralNet
Qimin Chen, Oscar Beijbom, Stephen Chan, Jessica Bouwmeester, David Kriegman
ICCV Workshop on Computer Vision in the Ocean (ICCVW), 2021
pdf / bibtex / coralnet / code

CoralNet is a cloud-based website and platform for manual, semi-automatic and automatic analysis of coral reef images. Users access CoralNet through optimized web-based workflows for common tasks, other systems can interface through API.

Working experience

Incoming Adobe Internship, May - TBC, 2024

Adobe Internship, May - November 2023

Teaching
  • TA - CMPT 762: Computer Vision SFU [Spring 22]
  • TA - CMPT 464/764: Geometric Modeling in Computer Graphics SFU [Fall 21]
  • Tutor - CSE 152: Introduction to Computer Vision UCSD [Spring 19]
Reviewer

SIGGRAPH Asia 2023, CVPR 2023, CVPR 2024



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