Qimin Chen

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

AI Conference Deadlines

CVF Open Access

3D Shape Analysis Paper List
Neural Rendering Paper List

Research

My research interest mainly focuses on Computer Graphics, especially geometric modeling, 3D shape generation and manipulation.

	DECOLLAGE: 3D Detailization by Controllable, Localized, and Learned Geometry Enhancement Qimin Chen, Zhiqin Chen, Vladimir G. Kim, Noam Aigerman, Hao Zhang, Siddhartha Chaudhuri ECCV 2024 pdf / code / project page DECOLLAGE is a learning-based method that enables novice users to add geometric details to a coarse 3D shape by selecting regions on it and assigning them the styles of exemplar shapes with compelling geometric details.
	DAE-Net: Deforming Auto-Encoder for fine-grained shape co-segmentation Zhiqin Chen, Qimin Chen, Hang Zhou, Hao Zhang ACM SIGGRAPH 2024 (Conference) pdf / code DAE-Net is an unsupervised 3D shape co-segmentation method that learns a set of deformable part templates from a shape collection, which yields high-quality, consistent, and fine-grained 3D shape co-segmentation.
	ShaDDR: Interactive Example-Based Geometry and Texture Generation via 3D Shape Detailization and Differentiable Rendering Qimin Chen, Zhiqin Chen, Hang Zhou, Hao Zhang ACM SIGGRAPH Asia 2023 (Conference) pdf / 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.
	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.
	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.

Working experience

Adobe Internship Seattle [May - Aug, 2024]
Adobe Internship Seattle [May - Aug, 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 2024, SIGGRAPH Asia 2023, CVPR 2024, CVPR 2023, ECCV 2024, AAAI 2024, Eurographics 2025

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