Fenggen Yu(余锋根)

I am Ph.D. from 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 and Analysis, Geometric Deep Learning, 3D Content Generation, and 3D Reconstruction from multi-sensors.

News


July 1, 2024. Three papers accepted to ECCV 2024.

May 26, 2024. I served in the SIGGRAPH Asia 2024 Committee as COI coordinator.

April 5, 2024. I successfully defended my Ph.D. thesis! The thesis is Learning Structured Representations of 3D CAD Models.

My doctoral graduation photoed by Yizhi Wang at SFU, 2024.

Selected Publications

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

DPA-Net: Structured 3D Abstraction from Sparse Views via Differentiable Primitive Assembly.

[Paper], Accepted to [ECCV 2024].

We present a differentiable rendering framework to learn structured 3D abstractions in the form of primitive assemblies from sparse RGB images capturing a 3D object.

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.

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