Fenggen Yu(余锋根)

I am 4th year Ph.D. student of Gruvi Lab, school of Computing Science at Simon Fraser University, under supervision of Prof. Hao(Richard) Zhang. I received my bachelor degree and master degree from Nanjing University, under supervision of Associate Prof. Yan Zhang. My research interests are on 3D Geometric Modeling, Geometric Deep Learning, Intelligent 3D Content Creation, Shape Editing and Neural Radience Field.


Sep 22, 2023. Our D2CSG is accepted to NeurIPS 2023.

June 1, 2023. I am co-organzing the 3D Vision and Modeling Challenges in eCommerce Workshop in ICCV 2023, welcome any submission to our workshop challenge!

June 11, 2023. I gave a talk at Amazon about A review of leanring 3D CAD model structure.

May 21, 2023. I am going to start my fourth internship as an Applied Scientist Intern in Amazon Imaging Science Team.

July 14, 2023. My work HAL3D at Amazon is accepted to ICCV 2023.

June 23, 2022. I am going to start my third internship as an Applied Scientist Intern in Amazon Imaging Science Team.

Mar 3, 2022. Our CAPRI-Net is accepted to CVPR 2022.

Hollywood photoed in California, 2023.

Selected Publications

(More in here)

Fenggen Yu, Qimin Chen, Maham Tanveer, Ali Mahdavi-Amiri, and Hao(Richard) Zhang.

D2CSG: Unsupervised Learning of Compact CSG Trees with Dual Complements and Dropouts.

[Paper], Accepted to [NeurIPS 2023].

We present D2CSG, 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.

Fenggen Yu, Yiming Qian, Francisca Gil-Ureta, Brian Jackson, Eric Bennett, and Hao(Richard) Zhang.

HAL3D: Hierarchical Active Learning for Fine-Grained 3D Part Labeling.

[Paper], Accepted to [ICCV 2023].

We present the first active learning tool for fine-grained 3D part labeling, a problem which challenges even the most advanced deep learning (DL) methods due to the significant structural variations among the small and intricate parts.

Fenggen Yu, Zhiqin Chen, Manyi Li, Aditya Sanghi, Hooman Shayani, Ali Mahdavi-Amiri and Hao(Richard) Zhang.

CAPRI-Net: Learning Compact CAD Shapes with Adaptive Primitive Assembly.

[Paper], [Project Page], Accepted to [CVPR 2022]

We introduce CAPRI-Net, a neural network for learning compact and interpretable implicit representations of 3D computer-aided design (CAD) models, in the form of adaptive primitive assemblies.

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