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.
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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.
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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.
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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.
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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
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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.
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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.
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Incoming Adobe Internship, May - TBC, 2024
Adobe Internship, May - November 2023
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TA - CMPT 762: Computer Vision
SFU [Spring 22]
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TA - CMPT 464/764: Geometric Modeling in Computer Graphics
SFU [Fall 21]
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Tutor - CSE 152: Introduction to Computer Vision
UCSD [Spring 19]
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SIGGRAPH Asia 2023, CVPR 2023, CVPR 2024
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Deep Learning and Keep Learning.
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